dataesr/scientific-paragraphs-categorization huggingface dataset:
All isolates of rep-PCR type 2 belonged to ST309, whereas isolates of rep-PCR types 1, 4, and 5 belonged to ST253, ST312, and ST252, respectively. Isolates of rep-PCR type 3 had an ST previously undescribed in the pubMLST database and now deposited as ST1753. Molecular typing showed that massive and persistent colonization of the DCUWs involved a P. aeruginosa clone of rep-PCR type 2 and ST309. Molecular identification and typing of Achromobacter isolates. A total of 78 Achromobacter isolates were recovered from DCUWs between July 2012 and July 2013. Molecular typing by multiplex rep-PCR of 17 representatives showed that colonization of the DCUW involved only 2 clones. The rep-PCR type A was observed for isolates recovered from July 2012 to July 2013, while the rep-PCR type B was observed only once for strains isolated in 5 different rooms in November 2012 (Table.
ACKNOWLEDGMENTS We are very grateful to  Chantal Gounelle ,  Sylvie Ivanoff ,  Christian Rouquette , and  Myriam Roura  from the  Infection Control Lab of the CHRU of Montpellier  for excellent technical assistance. We are also indebted to all members of the  Direction des Travaux et du Patrimoine of the CHRU of Montpellier , particularly to  Jean-Luc Amarger ,  Sébastien Soler , and  Emilie Roseau , for help in gathering technical data. This work was partly supported by the  Ministère de l'Enseignement Supérieur et de la Recherche, France , the  Islamic Bank of Development , and the association ADEREMPHA, Sauzet, France.
We also gratefully acknowledge the support of project staff at GSFC and Lockheed Martin for contributions to the design and execution of in-flight maneuvers and subsystem tests required to characterize and mitigate spacecraft magnetic fields. Data used in this study are available at the planetary plasma interactions planetary data system node (http://ppi. pds. nasa. gov/project/maven/). The Editor thanks two anonymous reviewers for their assistance in evaluating this paper. Geophysical Research Letters 10. 1002/2015GL065366.
It is an update of Le  Quéré et al. (2014) , including data to year 2013 (inclusive) and a projection for fossil fuel emissions for year 2014. The main changes from Le  Quéré et al. (2014)  are as follows: (1) we use 3 years of BP energy consumption growth rates  (coal, oil, gas)  to estimate E FF compared to 2 years in the previous version (Sect. 2. 1), (2) we updated S OCEAN estimates from observations to 2013 extending the Surface Ocean CO 2 Atlas (SOCAT) v2 database  (Bakker et al. , 2014; Sect. 2. 4 ) with additional new cruises, and (3) we introduced results from three atmospheric inverse methods using atmospheric measurements from a global network of surface stations through 2013 that provide a latitudinal breakdown of the combined land and ocean fluxes (Sect. 2. 6). The main methodological differences between annual carbon budgets are summarised in Table  3.
For countries without data in 2013 we use the 2012 values (zero growth). Some fraction of the CaO and MgO in cement is returned to the carbonate form during cement weathering, but this is generally regarded to be small and is ignored here. Emission estimates from gas flaring are calculated in a similar manner as those from solid, liquid, and gaseous fuels, and rely on the UN Energy Statistics to supply the amount   3. Main methodological changes in the global carbon budget since first publication. Unless specified below, the methodology was identical to that described in the current paper. Furthermore, methodological changes introduced in one year are kept for the following years unless noted. Empty cells mean there were no methodological changes introduced that year. of flared or vented fuel.
For emission years 2011-2013, flaring is assumed constant from 2010 (emission year) UN-based data. The basic data on gas flaring report atmospheric losses during petroleum production and processing that have large uncertainty and do not distinguish between gas that is flared as CO 2 or vented as CH 4. Fugitive emissions of CH 4 from the so-called upstream sector (e. g. coal mining and natural gas distribution) are not included in the accounts of CO 2 emissions except to the extent that they are captured in the UN energy data and counted as gas "flared or lost". The published CDIAC data set has 250 countries and regions included. This expanded list includes countries/regions that no longer exist, such as the USSR and East Pakistan. For the budget, we reduce the list to 216 countries by reallocating emissions to the currently defined territories.
This involved both aggregation and disaggregation, and does not change global emissions. Examples of aggregation include merging East and West Germany to the currently defined Germany. Examples of disaggregation include reallocating the emissions from the former USSR to the resulting independent countries. For disaggregation, we use the emission shares when the current territory first appeared. For the most recent years, 2011-2013, the BP statistics are more aggregated, but we retain the detail of CDIAC by applying the growth rates of each aggregated region in the BP data set to its constituent individual countries in CDIAC. Estimates of CO 2 emissions show that the global total of emissions is not equal to the sum of emissions from all countries. This is largely attributable to emissions that occur in international territory, in particular the combustion of fuels used in international shipping and aviation (bunker fuels), where the emissions are included in the global totals but are not attributed to individual countries.
, 2013; Peters et al. , 2011b Peters et al. , , 2012b)). The detailed estimates for 2004 and 2007 and time series approximation from 1990 to 2012 are based on an updated version of the GTAP database  (Narayanan et al. , 2013). We estimate the sector level CO 2 emissions using our own calculations based on the GTAP data and methodology, include flaring and cement emissions from CDIAC, and then scale the national totals (excluding bunker fuels) to match the CDIAC estimates from the most recent carbon budget. We do not include international transportation in our estimates of national totals, but we do include them in the global total. The time series of trade data provided by GTAP covers the period 1995-2009 and our methodology uses the trade shares as this data set. For the period 1990-1994 we assume the trade shares of 1995, while for 2010 and 2011 we assume the trade shares of 2008 since 2009 was heavily affected by the global financial crisis.
2. 2. 1) primarily based on net forest area change and biomass data from the Forest Resource Assessment (FRA) of the Food and Agriculture Organization (FAO), which is only available at intervals of 5 years and ends in 2010  (Houghton et al. , 2012). Interannual variability in emissions due to deforestation and degradation have been coarsely estimated from satellite-based fire activity in tropical forest areas (Sect. 2. 2. 2; Giglio et al. , 2013; van der Werf et al. , 2010). The bookkeeping method is used to quantify the E LUC over the time period of the available data, and the satellite-based deforestation fire information to incorporate interannual variability (E LUC flux annual anomalies) from tropical deforestation fires. The satellite-based deforestation and degradation fire emissions estimates are available for years 1997-2013.
The data are based on countries' self-reporting, some of which includes satellite data in more recent assessments  (Table 4 ). Changes in land cover other than forest are based on annual, national changes in cropland and pasture areas reported by the FAO Statistics Division  (FAOSTAT, 2010). LUC country data are aggregated by regions. The carbon stocks on land (biomass and soils), and their response functions subsequent to LUC, are based on FAO data averages per land-cover type, biome, and region. Similar results were obtained using forest biomass carbon density based on satellite data  (Baccini et al. , 2012). The bookkeeping method does not include land ecosystems' transient response to changes in climate, atmospheric CO 2 , and other environmental factors, but the growth/decay curves are based on contemporary data that will implicitly reflect the effects of CO 2 and climate at that time.
Tropical deforestation is the largest and most variable single contributor to E LUC. Fire emissions associated with deforestation and tropical peat burning are based on the Global Fire Emissions Database (GFED) described in van der Werf et al. (  2010 ) but with updated burned area  (Giglio et al. , 2013)  as well as burned area from relatively small fires that are detected by satellite as thermal anomalies but not mapped by the burnedarea approach  (Randerson et al. , 2012). The burned-area information is used as input data in a modified version of the satellite-driven Carnegie-Ames-Stanford Approach (CASA) biogeochemical model to estimate carbon emissions associated with fires, keeping track of what fraction of fire emissions was due to deforestation (see  van   Atlas et al. (2011). m Updated version "s81_v3.
, 2011) , which provided annual, half-degree, fractional data on cropland, pasture, and primary and secondary vegetation, as well as all underlying transitions between landuse states, including wood harvest and shifting cultivation. This data set used the HYDE  (Klein Goldewijk et al. , 2011)  spatially gridded maps of cropland, pasture, and ice/water fractions of each grid cell as an input. The HYDE data are based on annual FAO statistics of change in agricultural area  (FAOSTAT, 2010). For the years 2011, 2012, and 2013, the HYDE data set was extrapolated by country for pastures and cropland separately based on the trend in agricultural area over the previous 5 years. The HYDE data set is independent of the data set used in the bookkeeping method  (Houghton, 2003, and updates) , which is based primarily on forest area change statistics  (FAO, 2010).
Although the Hurtt land-usechange data set indicates whether land-use changes occur on forested or non-forested land, typically only the changes in agricultural areas are used by the models and are implemented differently within each model (e. g. an increased cropland fraction in a grid cell can be at the expense of either grassland or forest, the latter resulting in deforestation; land-cover fractions of the non-agricultural land differ between models). Thus the DGVM forest area and forest area change over time is not consistent with the Forest Resource Assessment of the FAO forest area data used for the bookkeeping model to calculate E LUC. Similarly, model-specific assumptions are applied to convert deforested biomass or deforested area, and other forest product pools, into carbon in some models (Table  5 ).
Using the difference between these two DGVM simulations to diagnose E LUC is not consistent with the definition of E LUC in the bookkeeping method  (Gasser and Ciais, 2013; Pongratz et al. , 2014). The DGVM approach to diagnose land-use-change CO 2 emissions would be expected to produce systematically higher E LUC emissions than the bookkeeping approach if all the parameters of the two approaches were the same (which is not the case). Here, given the different input data of DGVMs and the bookkeeping approach, this systematic difference cannot be quantified.
Uncertainty assessment for E LUC Differences between the bookkeeping, the addition of firebased interannual variability to the bookkeeping, and DGVM methods originate from three main sources: the land-coverchange data set, the different approaches used in models, and the different processes represented  (Table 5 ). We examine the results from the seven DGVM models and of the bookkeeping method to assess the uncertainty in E LUC. 1960-1969, 1970-1979, 1980-1989, 1990-1999, 2000-2009 , last decade, and last year available. All values are in GtC yr -1. The DGVM uncertainties represents ±1σ of results from the nine individual models; for the inverse models all three results are given where available. Mean (GtC yr -1 )  1960-1969 1970-1979 1980-1989 1990-1999 2000-2009 2004-2013 2013  Land-use-change emissions (E LUC ) Bookkeeping method 1.
The best resolved changes are in atmospheric growth (G ATM ), fossil fuel emissions (E FF ), and, by difference, the change in the sum of the remaining terms (S OCEAN + S LAND -E LUC ). The variations from year to year in these remaining terms are largely model-based at this time. Further efforts to increase the availability and use of annual data for estimating the remaining terms with annual to decadal resolution are especially needed. Our approach also depends on the reliability of the energy and land-cover-change statistics provided at the country level, and are thus potentially subject to biases. Thus it is critical to develop multiple ways to estimate the carbon balance at the global and regional level, including estimates from the inversion of atmospheric CO 2 concentration used here for the first time, the use of other oceanic and atmospheric tracers, and the compilation of emissions using alternative statistics (e.
Mexico is undergoing rapid population ageing as a result of its epidemiological transition. This study explores the interface between this rapid population ageing and the burden of cancer. The number of new cancer cases is expected to increase by nearly 75% by 2030 (107,000 additional cases per annum), with 60% of cases in the elderly (aged ≥ 65). A review of the literature was supplemented by a bibliometric analysis of Mexico's cancer research output. Cancer incidence projections for selected sites were estimated with Globocan software. Data were obtained from recent national census, surveys, and cancer death registrations. The elderly, especially women and those living in rural areas, face high levels of poverty, have low rates of educational attainment, and many are not covered by health insurance schemes.
Literature review A literature search was performed on 14 April 2015 using the following databases: PubMed, Medline, and Embase. Search terms were 'cancer' and 'gerentology', or 'elderly' or 'older adult', or 'geriatric' and 'Mexico', not New Mexico. Appropriate synonyms for cancer were utilised. Articles not available in English or Spanish were excluded. Additional searches were performed of 'Mexico' not New Mexico, plus one of the following terms: 'cancer screening', 'chemotherapy', 'radiotherapy', 'geriatric oncology', 'social health insurance', 'catastrophic health expenditures', and 'out of pocket payments'.
Analysis of Globocan database In the absence of a national cancer registration system in Mexico (The 'Registro Histopathalogico de Neoplasias Malignas' (RNHM) was disbanded in 2004), accurate population cancer incidence is unavailable. The International Agency for Research on Cancer has compiled estimates of the worldwide incidence and mortality for 28 cancers in 184 countries  [28]. Using this database, it was possible to estimate current and future (2030) cancer incidence in Mexico.
Bibliometric analysis Bibliometrics is the quantitative analysis of research outputs and thus a validated, surrogate for overall research activity. Papers relevant to cancer research, from Mexico were identified in the Web of Science (WoS) database by means of a complex filter containing lists of specialist cancer journals and of title words indicative of cancer research, as previously described  [30]. The bibliographic details of articles, notes, and reviews with an address in Mexico and published between 1989 and 2012, were downloaded to a series of files. Mexican cancer papers relevant to each of 16 cancer sites listed by the WHO in its Global Burden of Disease study, and four additional sites (brain, gallbladder, kidney, and testes) of importance for Mexico, were identified within the spreadsheet by means of a series of subfilters.
Population household surveys Microdata from the Censo de Poblacion y Vivienda 2010 (National Housing and Population Census) were used to provide information on sources of income, employment status, levels of poverty, social security, and health services coverage of older persons  [32]. A previous analysis by the National Population Council was used to guide this current review  [33]. Key differences between male and female populations and rural and urban inhabitants were identified using pre-defined census criteria. Microdata from the Study on Global Ageing and Adult Health (SAGE Mexico 2009-2010) were used to identify key sociodemographic characteristics in the elderly, including education status and marital status. The survey was developed by the WHO to ascertain patterns of health and well-being of adult populations in six low-and middle-income countries, including Mexico, China, and India  [34].
Cancer incidence projections to 2030 Using Globocan 2012 software, we estimated that the effect of the demographic transition will result in an extra 107,000 cancer cases per year in Mexico by 2030 (a 72% increase compared to 2012). Globocan estimates predict that 58% of these cases will be in men and women ≥ 65, despite this group representing approximately 11. 5% of the population  [36]. We reviewed cancer site-specific incidence projections. There are expected to be an additional 7,900 new cases of lung cancer per annum by 2030 (8,600 cases in 2012), 75% of these new cases will affect individuals aged ≥ 65 years, with a greater proportion in males. For colorectal cancer, there are expected to be approximately 7,072 additional new cases per year by 2030 (80% increase based on 2012 estimates) of which 62% will be in men and women aged ≥ 65  [36].
The quantity of biomass produced during the growing period strongly depends on the CGR and on the amount of N acquired by the plants depends on the CNR  [16, 26]. We assumed that CGR and CNR are two relevant complementary indicators reflecting the ability of cover crops to function as a nitrate catch crop and a producer of green manure for the subsequent cash crop  [1, 2]. Leaf traits were measured at two experimental locations to test the sensitivity of the leaf functional trait approach to differences in environmental conditions (soils and climates in particular). To better understand where these cover crop species lie in previously identified axes of resource-use strategy variation  [27]  and verify the robustness of the method, these experimental data were compared to those from two large published data sets established for a large number of wild species (S1 Text): (i) one corresponding to the well-known Leaf Economics Spectrum  [17]  and (ii) the second one describing the SLA and LDMC relationship corresponding to the trade-off between acquisitive and conservative resource  [28].
Data analysis Co-variations among traits were evaluated using principal component analysis (PCA). We assessed the position of cover crop species along the LES axis of variation using the relationship between SLA and LNC established by Wright et al. [17]. We did the same for the SLA-LDMC relationship established for 1392 wild herbaceous species based on a compilation of published data (S1 Text). The position of species on the CSR triangle was assessed with ternary coordinates calculated from LA, LDMC and SLA values, as described by Pierce et al. [24]. Predictive regressions of this method translated PCA coordinates in multidimensional space into coordinates in a strategy triangle. As a first analysis of the possible effect of domestication and breeding on species traits, we compared the trait values measured on cover crops at the experimental sites to those for annual wild species of the same genus available in the TRY database  (Kattge et al.
2011 [34]  and S2 Text). Leaf traits were extracted from the database for all native species of the same genera as those used in our experiment. For each trait, the mean value for all species within the wild genus was calculated and compared to the values obtained for the corresponding genus in our experiment. On average, four congeneric wild species were available within a wild genus for that comparison. For buckwheat (Polygonum fagopyrum or Fagopyrum esculentum), we used the wild species from the Polygonum genus. Statistical analyses, correlations and generalized linear modelling (GLM) with stepwise regression procedure (stepAIC, P<0. 05) were performed using the statistical software package R (version 2. 14. 0).
A first hypothesis explaining a cover crop domestication syndrome The hypothesis tested here is that domestication or breeding modified the values of leaf functional traits of cover crops and thus their leaf traits syndrome, i. e. the combination of leaf traits values for a given species. Examination of Fig. 5  suggests that constraints exerted by domestication have been stronger on LDMC than on SLA, as shown by the narrowest range of variation of the former trait in cover crops. We tested this hypothesis further by comparing leaf traits values of cover crops with those of their homologous wild annual species from the same genus taken from the TRY database. The analysis was done at the genus level because no data was available to confidently identify the wild ancestor of each cover crop species (Table  3 ).
The comparison indicated that cover crop species presented systematically and significantly higher SLA and LNC but lower LDMC than wild species. Moreover, Fabaceae which did not receive any fertilizer in our experiment also presented a significant higher LNC than wild species (Table  3 ), suggesting that the comparison of leaf trait values between the TRY database and our experimental data is valid. This is also consistent with results found by Kazakou et al  [36]  who showed that SLA, LDMC and LA are not very sensitive to N fertilisation. The impact of domestication seems to have been stronger on LDMC and LNC than on SLA, based on the distance in trait values between cover crops and wild species: LDMC and LNC were respectively 37% lower and 48% higher in cover crops, while SLA was only 24% higher.
Traits of wild species are means of annual species of same genus. Values are means ± SDs of species of each family (na: data not available in TRY database). The significance of differences was assessed by Student's t-tests, testing the global effect for all genera. The relative distance was calculated as the difference between the average values of cover crops and the average values of wild species as: Relative distance rate = (Cover crop trait valuewild species trait value) / wild species trait value. doi:10. 1371/journal. pone. 0122156. t003.
Acknowledgments The authors would like to thank  M. Labarrère ,  L. Bossut  and  U. Steiner  for their efficient technical help. We also thank  A. Gavaland ,  P. Bataillon  and  D. Campergue  at the  INRA Auzeville experimental unit  for technical assistance. We are grateful to  J. Kattge , coordinator of the TRY database, and all of its contributing authors (see S2 Text) for allowing us to use their data from the TRY database.
This study was supported by  Arvalis-Institut du Végétal , the  Midi-Pyrénées Region , the  UMR AGIR lab, INRA (Institut national de la recherche agronomique)  and the  ANR (Agence nationale de la recherche)  (project  ANR-09-STRA-06 ;  MicMac-design ,  Programme STRA 2009 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This means that the linguistic data (here speech) is not handled in a specific way but rather in a symmetric manner with respect to other perceptual modalities. Finally we explore the question of the structure and (de)composition of concepts. In particular we show that it is possible for an artificial system to discover subcomponents of perception, such as words in spoken sentences, although the system is only exposed to a task that requires to associate whole sentences to corresponding scenes. The next section presents the experimental framework as well as the datasets used in the experiments. Section Methods details the algorithms that forms the MCA-NMF learner, including the low-level processing of sensor data. Section Experiments provides the results and their analysis for the experiments on learning multimodal concepts as cross-modal associations.