dataesr/scientific-paragraphs-categorization huggingface dataset:
Cross-sectional data analysis Table  1  presents the age of the top ten women and men for each Ironman race held in 2010. In 19 of the 20 analyzed Ironman qualifiers held in 2010, there was no age difference between women and men (p > 0. 05). The only age difference between the genders was seen in Ironman Canada where men were older than women (p = 0. 023). For all 20 races (19 qualifier and Ironman Hawaii as the World Championship), the age of peak Ironman performance was 32. 2 ± 1. 5 years for men and 33. 0 ± 1. 6 years for women, respectively (p > 0. 05). When Ironman Hawaii was excluded, the age of peak Ironman performance was 32. 0 ± 1. 4 years for men and 33. 0 ± 1. 6 years for women, respectively (p > 0. 05).
Age of peak ironman performance Previous studies regarding the age of peak endurance performance showed that the age of peak performance was relatively constant over centuries because of the large role played by biological factors  [1, 2]. Data about the age of peak endurance performance can be useful in order to gain more detailed information about the physical development of humans  [1]  and to plan an athletic career. However, the potential gender difference in the age of peak endurance performance has been sparsely investigated and controversially discussed  [1, [4] [5] [6]. The present data showed that both male and female Ironman triathletes reached peak Ironman performance at the same age of around 32-33 years. Even if the Ironman triathlon is young in the field of ultraendurance events with the first Ironman taking place in Hawaii in 1978  [18] , and Ironman triathletes started with triathlon later in their life coming from a variety of sporting backgrounds with most from running, followed by swimming and cycling  [22] , the finding of no gender difference in the age of peak performance was constant over the 16year period analyzed in Ironman Switzerland and confirmed by the cross-sectional analysis of all Ironman triathlons held in 2010.
Diagnosis The diagnosis of PCCD in an index patient is based on clinical data including history, family history, and 12-lead ECG. The potential presence of congenital heart disease and/ or cardiomyopathy must be investigated by 2-D echocardiography or other imaging modalities, such as cardiac MRI. Early-onset PCCD in the absence of structural heart disease should prompt consideration of PCCD genetic testing, particularly if there is a positive family history of conduction abnormalities, pacemaker implants, or sudden death. 1  (Targeted) genetic testing may be considered as part of the diagnostic evaluation for patients with either isolated PCCD or PCCD with concomitant structural heart disease, especially when there is documentation of a positive family history of PCCD. 1.
Acknowledgments The authors thank  I. Cherednik ,  I. Krichever ,  A. Orlov ,  T. Takebe  and  P. Wiegmann  for discussions. The work of A. A. was supported in part by  RFBR  grant  10-02-00509 , joint  RFBR  grant  09-02-93105-CNRS ,  ANR grant GranMA  ( BLANC-08-1-313695 ) and by the  ERC   Starting Independent Researcher Grant  StG No. 204757-TQFT. The work of V. K. and A. Z. was supported in part by  RFBR  grant  11-02-01220. The work of V. K. and S. L was also partly supported by the  ANR grants StrongInt  ( BLANC-SIMI-4-2011 )  GranMA  ( BLANC-08-1-313695 ) and by the  ESF  grants  HOLOGRAV-09-RNP-092  and ITGP. The work of Z. T. is supported by  SFB647  " Space-Time-Matter ". A part of this work was done when he was at  Max-Planck-Institut für Gravitationsphysik, Albert-Einstein-Institut.
Project objectives The fundamental goals of this project are to characterise and understand the mineralogical, geochemical, petrophysical and geomechanical consequences of long-term exposure of supercritical CO 2 , CO 2 -gas and CO 2 -charged fluids on reservoir rocks, cap rocks and fault zone materials. This will improve our predictions of the long-term security of anthropogenic CO 2 geological storage sites. Data gathered during the extensive laboratory study of samples collected during drilling will be used to define or verify coupled models, like reactive transport (flow and geochemical reactions) or geochemical-mechanical models; these are ongoing projects at Shell Global Solutions International and the University of Cambridge. Despite the wide occurrence of natural CO 2 reservoirs, by their nature the critical parts of the reservoirs are buried and can only be accessed by drilling.
ACKNOWLEDGMENTS The co-authors wish to thank  Dr. Steve Osofsky  of the  WCS-Ahead Programme  and  Dr. Gavin Thomson of TAD Scientific  for helpful comments on the manuscript. SC, DTH and TL are grateful to the initiative of  BBSRC DFID Scottish Government  ( Combating Infectious Diseases of Livestock for International Development ) for supporting TL (grant no. BB/H009302/1 ) and providing funding for a workshop in which some of these ideas were developed. AC was supported by the research platform ''Production and Conservation in Partnership''. DJP is a BBSRC funded Jenner Investigator.
The X. nematophila strain F1, isolated from the nematode Steinernema carpocapsae "Plougastel" from Brittany, France, was also highly genetically documented (for example, see references 7-17), but no genomic data were available. Therefore, we sequenced the genome of this highly studied strain. The genomic DNA of X. nematophila strain F1 was purified from our laboratory stock according to the method of Brenner et al. (18). Sequencing with the Illumina HiSeq 2000 sequencer resulted in 24,259,054 single-end reads with a length of 36 nucleotides (performed by Montpellier MGX genomix, Montpellier, France) and in 60,622,006 mate-pair reads of a 3-kb insert size library with a length of 50 nucleotides (performed by GATC Biotech AG, Konstanz, Germany). Single-end reads were contiged using SOAPdenovo 1.
05  (19) , and then contigs were scaffolded with LYNX (J. Gouzy, unpublished data) by utilizing mate-pair information. Final assembly consisted of 61 scaffolds with 351 contigs comprising a total length of 4. 3 Mb (4. 2 Mb without undetermined bases). The assembly has an N 50 scaffold size of 401,015 nucleotides and a GC content of 43. 64%. Functional annotation was carried out using tools of the MicroScope platform  (20) , and the annotated genome was implemented in the public XenorhabduScope database (https://www. genoscope. cns. fr/agc /microscope/home/index. php). The assembly contains 4,325 genomic objects, among which are 4,245 coding sequences, 3 rRNA genes, 47 tRNA genes, and 30 noncoding RNAs. 3,716 coding sequences (CDS) (88% of the whole CDS) of the X. nematophila F1 genome are also present in X.
nematophila ATCC 19061 and are considered the X. nematophila species core genome. Among the 529 CDS specific to X. nematophila F1, 420, 45, and 3 are annotated as proteins of unknown function, phagic proteins, and transposases, respectively. The remaining CDS are mainly annotated as involved in metabolism, transport, and plasmid mobilization. The genome information provided here will allow for genomic experiments such as RNA-Seq and ChIP-Seq analysis on the X. nematophila F1 strain. Nucleotide sequence accession numbers. This whole-genome sequence of Xenorhabdus nematophila strain F1 has been deposited at the European Nucleotide Archive under the accession number CAVM000000000. The version described is accession no. CAVM000000000. 1.
Fossil fuel emissions Current datasets of fossil fuel CO 2 emissions averaged by country, sector and year are maintained by the International Energy Agency (IEA) (IEA, 2012) (http://www. iea. org/co2highlights/co2highlights. pdf), the Carbon Dioxide Information Analysis Center (CDIAC)  (Boden et al. , 2012)  based on the UNSO energy data set, and EDGAR4. 2 (a product of the Joint Research Center of the European Commission (JRC) together with the Netherlands Environmental Assessment Agency (PBL) (EDGAR4-database, 2009) based on IEA energy data set. Emission maps exist over the globe from different data products only at 100 km spatial resolution  (Andres et al. , 1996)  usually with very limited temporal information (although  Andres et al. , 2011)  now provide global estimates on a monthly time scale).
Air sea flux calculation from pCO 2 requires knowledge of gas transfer velocities, which depend on wind speed, adding uncertainty to flux estimates from pCO 2 measurements. A key ongoing international effort, the Surface Ocean CO 2 Atlas (SOCAT) aims to synthesize pCO 2 data collected over the last 40 years into a quality controlled data base, along with uniform metadata, that can be used to examine pCO 2 variability over a range of time and space scales  (Pfeil et al. , 2013; Sabine et al. , 2013). The current version of the SOCAT pCO 2 database contains more than 6 million observations collected by research ships, commercial volunteer ships and moorings (Fig. 5 ). It is expected to reach more than 10 million in the second release. Over the best-sampled ocean regions, such as the North Atlantic and the equatorial Pacific, mean air-sea CO 2 fluxes can be reconstructed to within 20 %, and their interannual variation to within 10 %  (Watson et al.
Forest biomass inventories Developed countries have forest-cover change mapping programs and national forest inventories that span decades and contain data from a large number of sampling locations (e. g. > 150 000 in the US). Forest inventories were set up to inform the national wood industry and therefore often exclude woodlands that do not contribute to wood harvest. Tropical countries have little or no inventory data, nor have comprehensive estimates of all relevant carbon pools, except for research networks such as RAIN-FOR in South America  (Phillips et al. , 1998 (Phillips et al. , , 2009)) , and CARBOAFRICA  (Henry, 2010) ,  AFRITRON (Lewis et al. , 2009 ) and other regional studies (e. g. Ryan et al. , 2011)  in Africa. The main source for global datasets covering each country remains the periodic reports of the Food and Agriculture Organization (FAO) Forest Resource Assessment  (FAO, 2009 (FAO, , 2010)).
River CO 2 outgassing/carbon burial in reservoirs There is no coordinated observation network of river carbon fluxes. The GLORICH database  (Hartmann et al. , 2009)  provides measurements of pCO 2 in rivers with ∼ 6000 data, but incomplete spatial coverage. There is no global dataset of carbon burial in lakes and reservoirs. Recent data syntheses (involving hydrological modeling on land) have improved our ability to quantify carbon delivery from land to estuaries and oceans  (Mayorga et al. , 2010). The CMTT (Continental Margins Task Team from IGBP core projects JGOFS and LOICZ and IMBER) synthesized carbon budgets from all major continental margins including the proximal as well as distal domains  (Liu, 2010). The ongoing international SOCAT project (see Sect. 2. 4. 1) includes a specific surface pCO 2 data synthesis for coastal seas.
Further, these maps are not temporally discrete (data from multiple years were used), and the main source of data, from ICEsat, no longer exists. A replacement mission may launch in 2016. Preliminary results using polarimetric interferometric SAR (PolInSAR) approaches have demonstrated sensitivity to biomass in some high biomass ecosystems  (Treuhaft et al. , 2003; Hajnsek and Papathannassiou, 2009). Several research programs are underway to implement the use of Synthetic Aperture Radar (SAR) as well as airborne/spaceborne Lidar, to derive estimates of vegetation aboveground biomass (e. g. Saatchi et al. , 2007). Satellite missions such as the BIOMASS P-band radar of ESA  (Le Toan et al. , 2011)  or a concept based upon the DESDynI mission of NASA (  (Hall et al. , 2011) ; www. desdyni. jpl.
nasa. gov/) are currently being considered for this purpose. maps at 1 km  (Friedl et al. , 2002). However these two products cannot be compared to detect land cover change. Global changes in forest cover have been derived from AVHRR at decadal increments  (Hansen et al. , 2003)  and MODIS at five-year increments  (Hansen et al. , 2010). In some regions, particularly tropical forests of the Amazon basin, deforestation is monitored on a regular basis using the INPE PRODES system (www. obt. inpe. br/prodes/), though most tropical countries do not have operational forest monitoring systems in place  (DeFries et al. , 2007). Most land cover analysis to date has relied on low-resolution (> 300 m) satellite-based optical data. Satellite observations using Synthetic Aperture Radar (SAR) are also beginning to provide land-surface information, in particular over cloud-affected regions in the tropics and high-latitudes, where optical data are sparse.
A systematic acquisition strategy was developed for the ALOS L-band Synthetic Aperture Radar (PALSAR) for global tracking of land-use change. Up to its failure in 2011, ALOS-PALSAR provided five years of systematic global forest (and wetland) observations and the first systematic global observations for generating forest change as well as derived biomass maps in low biomass zones  (Saatchi et al. , 2011). The GEO-initiated GFOI aims to provide better access to Landsat-resolution optical and imaging radar data worldwide in support of REDD+ projects and/or national MRV programs. There is, however, no current capacity to generate repeated global biomass maps for determination of biomass change  (Houghton et al. , 2009). Such a capacity will better constrain the magnitude of land carbon storage changes, of biomass accumulation in forest and subsequent carbon sinks, and their locations.
Fires The AVHRR fire record goes back to 1982, providing the longest global record of fire but the data are sparse and incomplete. The European Space Agency (ESA) World Fire Atlas  (Arino and Plummer, 2001; Arino et al. , 2012) , provides another long continuous global record of active fires using the ATSR instrument, and has recently been upgraded using a new night-time algorithm and extended back to 1991. Other polar orbiting satellites, such as NOAA-AVHRR, Visible and Infrared Scanner (VIRS) on board the Tropical Rainfall Measuring Mission (TRMM), and geostationary satellites (Meteosat Second Generation-MSG; Geostationary Operational Environmental Satellites -GOES) extend these observations to better characterize the diurnal cycle of active fires  (Pu et al. , 2007; Ji and Stocker, 2002; Beaudoin et al.
The Global Fire Emission Database, GFED (Van der  Werf et al. , 2003; www. globalfiredata. org ) is an integrated product, combining MODIS, ATSR, and VIRS satellite products of fire activity with the Carnegie Ames Stanford Approach (CASA; http://geo. arc. nasa. gov/sge/casa/investg. html) terrestrial ecosystem model to estimate fire emissions of CO 2 , CH 4 and other species from 1997 to the present (Van der  Werf et al. , 2010). Other products also exist, but, even if the spatial and temporal pattern of fires is relatively well documented, the uncertainties in global fire emission estimates remain substantial  (Csiszar et al. , 2009). An assessment of emissions estimates from several fire emission products, including GFED, showed differences in model assumptions, including canopy consumption and changes in fuel moisture, were the main causes of uncertainties, and results varied by location, vegetation type and fire weather  (French et al.
Generation of integrated data-products from current observations Integrated data products from the GLOBALVIEW (www. esrl. noaa. gov/gmd/ccgg/ globalview/), GLOBCOLOUR (www. globcolour. info/), GLCN (www. glcn. org); GLOB-COVER (www. gofc-gold. uni-jena. de/), GLODAP (www. cdiac. ornl. gov/oceans/glodap/), SOCAT (www. socat. info/), Climate Change Initiative (http://www. esa-cci. org/), FLUXNET (www. fluxnet. ornl. gov/) and FAO Global Forest Resources Assessments (www. fao. org/forestry/) have improved quality and accessibility to data. Modeling projects such as CarbonTracker  (Peters et al. , 2007) , TransCom  (Baker et al. , 2006; Law et al. , 2008b) ; GEMS-MACC-MACC-II (www. gmes-atmosphere. eu/), CCDAS  (Rayner et al. , 2005)  integrative effort has been placed on CO 2 , much less on CH 4.
Back Close Full access to the raw observational data, calibration and validation data, core models and analysis software, intermediate and final data products, and documentation describing the processes and procedures for collecting, assimilating, and analyzing the data. All information products must include metadata to support traceability and independent reconstruction of the production process. In addition to robust products, a policy relevant carbon observing system will need to include services in the form of providing sustained end-user access to expert practitioners involved in data collection, modeling, data assimilation, and analysis. The observing system will have to meet the needs of operational agencies as well as those of research. Of these, operational requirements are probably more stringent or, at least, require more precise definition.
Back Close Full advection (including inter hemispheric exchange), and stratosphere-troposphere exchange, by using tracer and meteorological measurements (e. g. boundary layer height data from Lidar and Rawisond measurements, aircraft vertical profiles CO 2 and tracers). 2. Pursue regular transport models inter-comparison and evaluation against measurements, for CO 2 and CH 4 , and transport tracers (SF 6 , Radon-222), under programs such as TRANSCOM (www. transcom. project. asu. edu). Benchmarking and inter-comparison exercises should be designed and carried out for a range of spatial scales and terrain, including at regional and cities-scale. 3. Develop atmospheric transport models at higher than current resolution. A target in one decade would be a spatial resolution of 10-50 km over the globe for transport models used in inversions, and zooms of 1-10 km over regions/cities.
Note that the forward model of the MACC-II system (http://www. gmes-atmosphere. eu/ d/services/gac/nrt/nrt fields co2/) is currently run with a resolution of 15 km over the globe. 4. Work cooperatively with weather forecast centers (NCEP, ECMWF, GMAO,. ) to improve atmospheric transport models and products best suited for accurate transport of long-lived tracers like CO 2 and CH 4. 5. Possibly consider the utility and need for leveraging and expanding existing airquality inversion systems employed by some regulatory agencies (e. g. CMACS) to include CO 2 and CH 4 regional inversions. In terms of atmospheric data infrastructure, improving the current infrastructure to accommodate the required measurements will require to: 1. Implement inter-operable database tools, common formats, harmonized reporting of data uncertainties and calibration, for CO 2 , CH 4 and above-listed tracers.
Ocean in situ data To observe the temporal evolution of the ocean sink as well as the chemical changes it induces, i. e. , ocean acidification, the current observing system needs to be increased in sampling capacity and frequency. This can be achieved by combining existing programs and technology with new ones that take advantage of the emerging autonomous technology. The following notional requirements need to be considered: 1. Expansion of surface ocean pCO 2 observing systems using Volunteer Observing Ship lines (VOS = voluntary observing ships; commercial ships equipped with autonomous measurement systems) and autonomous surface craft and moorings. For instance, to determine the regional air-sea flux of CO 2 to within ±0. Naturally in these areas the potential aliasing of measurements through moving fronts etc.
Back Close Full 2. Sediment traps at more shallow depths in conjunction with measurements of dissolved quantities to derive a more precise quantification of changes in the biological carbon pumps. 3. Address potential gaps in future ocean color records. The MODIS instruments are currently in extended operational phases. NPP and JPSS VIIRS instruments, intended to provide a replacement for MODIS, will not have the necessary performance to fulfill this need; NASA is planning to expedite a follow-up capability (PACE) but there is a risk of a 2-5 yr data gap in the middle of this decade. For ESA, OLCI (Sentinel-3) will replace MERIS (see Fig. 7b ). 4. Develop advanced multispectral/hyper-spectral satellite sensors with new channels for detecting dissolved organic carbon (DOC) and phytoplankton functional types.
Biomass and soil carbon pools inventories from in-situ sampling Standardized inventory methods and data analyses, greater sampling density for nonliving C pools and disturbed areas define the main needs / requirements to link results from observations like FLUXNET, from radar/lidar missions (e. g. PALSAR) or atmospheric inversions that measure the variability of land-atmosphere fluxes, to the slowly changing carbon pools (e. g. forest biomass) representative of time-accumulated fluxes and implicitly include lateral (horizontal) time-accumulated fluxes. Regarding non-forest biomes, soil carbon inventories that are extremely sparse in the current observing system, and priority needs are: 1. Sampling of tropical and arctic regions that contain large soil carbon pools, including peat (tropical and northern), wetlands and mangroves, tundra and frozen soils.
Back Close Full large. Instead, we need a few large stems in each plot, i. e. 1 ha minimum. A scale of ∼ 1 to 4 ha defines the main spatial resolution requirement for satellite observations of biomass. Transformational change in the current sampling scheme will require a high priority to develop forest inventories in tropical forests. It is recommended to repeat measurements typically every 5 yr for trees while allowing 10 yr for the soil compartment to track pool dynamisc. Improved forest inventories systems as part of an observing system to quantify CO 2 fluxes from pools changes will need to fill in the following gaps: 1. Increase the number of tropical inventories, both over intact and disturbed forest sites and over closed canopy and woodland. 2. Make more accessible the current forest inventory data (at present mostly proprietary), including a subset of plot-level data or aggregated data at higher resolution than is presently available.
3. Harmonize the different methods for biomass inventory data collection to create continuous, standardized, geo-referenced forest biomass data products. 4. Refine standard methodology for biomass values for use in grid cells to include probability-based sampling across regions (e. g. systematic grid design, variable radius subplots in forests adjusted to maximum coefficient of variation < 20 %). 5. Complement data from forest inventories by additional sampling of carbon in soils, dead wood and woody debris. 6. Perform additional sampling of forest recently disturbed by events such as wind thrown, wildfires and insects to assess impacts on carbon stock changes. zones and fertility classes. Allometric functions are needed in a similar range of conditions to convert aboveground to total biomass.
Back Close Full 2. Validation campaigns to improve LAI, FAPAR and GPP, NPP remote sensing products should be initiated over sites (e. g. FLUXNET, inventories) distributed globally to sample a large number of biomes, land use and canopy structural types. Optimal site distribution and benchmarking should capitalize on current 3D radiation transfer modeling capabilities (e. g. Huang et al. , 2008). In terms of algorithm and products development, specific data products allowing regular assessment of carbon fluxes from land use / land cover change and disturbances will be required in the next decade (see Fig. 7c  for existing and planned satellite measurements): 1. Annual validated land use/land cover mapping products. Validation and accuracy assessment of satellite-derived land cover will be needed against independent data collected in ground surveys or aerial photos.