dataesr/scientific-paragraphs-categorization huggingface dataset:
Clades were referred to using the name of the basalmost taxon included followed by the sign " + "  (Amorim, 1982) , such that the type genus Lophops was included in Sarebasa +. This author did not make any distilled water. Final observations were done in glycerine. A few drops of blue paragon were added to the preparation in order to facilitate the observation of the ectodermic genital ducts. A Leica MZ16 stereomicroscope, equipped with a camera lucida for the drawing of the hind wing, tarsal segments and male genitalia, was used for observations. Photographs of the specimen were taken using a Nikon EOS 6OD camera attached to a Nikon SMZ 1500 stereomicroscope and further refi ned using Combine ZP software. For the genus and species described here, only two authors were selected for the authorship: M.
Data processing Feature extraction was performed with the Feature Extraction 9. 1 software (Agilent Technologies). For each experiment, the raw data sets were automatically normalized by the Feature extraction software using the CGH_1105_oct12 FE protocol browser. Analysis was performed with the Agilent Genomic Workbench 5. 0 software. The logratio  [4]  timing profiles (Fig. 3 ) were smoothed using the Triangular Moving Average option of the Agilent Genomic Workbench 5. 0 software with the Triangular algorithm and 500-Kb windows. The algorithm from CGH applications of the Agilent Genomic Workbench 5. 0 software was used in order to determine the replication domains (early and late domains). The aberration detection algorithms associated with the Z-score and with a threshold of 1. 8 define the boundaries and magnitudes of the regions of DNA loss or gain corresponding to the late and early replicating domains respectively (Regions with a positive log-ratio is replicated early and late when the log-ratio is negative).
Acknowledgements We acknowledge the  ImagoSeine core facility  of the  Institut Jacques Monod , also associated with  IBiSA and France BioImaging infrastructures. This work was supported by  ANR  grant ( ANR-14-CE10-0008-02 ) and by  Ligue Contre le Cancer Comité de Paris  ( RS16/75-108 ). We also acknowledge  Philippe Pasero  and  Jean-Sébastien Hoffmann  for cell lines.
Results and Discussions In the great majority of cases, a good agreement was obtained between MCNP5 and PE-NELOPE 2008: the difference between values obtained by the two codes is generally smaller than 0. 5%. In Tables 4, and 5 the calculated values of several conversion coefficients, in "kerma approximation" and in terms of absorbed dose are presented for mono energetic photons. The values of the coefficients presented in these three tables are mean values of coefficients calculated by MCNP5 and by PENELOPE 2008 calculation. The conversion coefficients have been compared to the data from the ICRU 57 Report (ICRU no. 57 1998) for photon energy up to 10 MeV. In the range 10 keV -2 MeV, we have found a good agreement between the coefficients calculated in "kerma approximation" and in absorbed dose, with those presented in the ICRU 57 Report.
24. , pp. 182) is visible for energies higher than 2 MeV. As for the h * k  (10)  coefficients, it is explained by the difference between the two sets of air kerma values. Two new sets of conversion coefficients has been calculated replacing total air kerma by collision air kerma for h*k and hk,p (Figures  12  and 13 ). Looking at these sets of data, the discrepancies with ICRU 57 values do not exist anymore. This leads to the conclusion that the ICRU 57 data set was calculated with the collision air kerma rather than the air kerma, and therefore that the data of table A1 page 59 of ICRU 57 are related to collision air kerma. The coefficients using collision air kerma are given in Table  9. Finally, the mean CC's over the whole spectrum produced by the LINAC were calculated using two assumptions: the first taking into account the collision air kerma and air kerma approximation as in ICRU 57, and the second accounting for the full transport of secondary particles (therefore in line with the definition of the dose equivalent, that is to say taking into account the energy really absorbed locally).
036 Gy/h at 1. 45 m and 3. 45 from the target respectively. The standard uncertainty is equal to 2. 3%. Monte Carlo simulations were used to calculate the conversion coefficients from air kerma to dose equivalents quantities above 10 MeV up to 20 MeV. The results show a good agreement with the ICRU 57 data taking into account the kerma approximation for the calculations and the collision air kerma rather than the total air kerma. This demonstrates that the conversion coefficients published in ICRU 57 were related to collision air kerma. Using these results the conversion coefficients for the whole LINAC spectrum were calculated. Taking into account that the LINAC produced pulsed radiation, this new radiation field can be used to calibrate any passive dosimeters and ionization chambers. Despite the fact that for Geiger-Muller and proportional counters the instantaneous dose rate is too high, leading to a saturation of detectors, calibrations of passive dosimeters in a high-energy photon beam are now available at LNE-LNHB.
1, WAL-7855 and CM201. 1) are available in public databases. The aim of this study was to analyse the genomes of six clinical isolates of C. bolteae and six clinical isolates of C. clostridioforme in order to identify genetic specificities between these closely related species. We focused our analysis on (CDSs coding for) functions, which could have a physiological effect within microbiota, and provided the catalogue of antimicrobial resistance genes of the two opportunistic pathogens.
Identification of antibiotic resistance determinants Drug resistance genes that had not been recognized by automated annotation were identified by homology sequence research on ARDB. Resistance genes were predicted on a value up to 40 % identity (50 % of positive substitutions) on 70 % of length above the cut-off value usually recommended (see list in Table  3 ). Then, the gene content and genetic organization of microbial resistance loci of the six C. bolteae and the six C. clostridioforme were compared to previous data obtained from C. clostridioforme CM201. 1 in our laboratory. Because sequence-based predictions might potentially identify determinants that do not lead to antimicrobial resistance, susceptibility testing was performed to obtain information on the predicted response of bacteria to antibiotics.
The strains included in this study showed resistance patterns, including ampicillin, macrolides, lincomycin and quinolones, now common in anaerobes (Table  4 ). Both genomic data (CDSs and annotations) and phenotypic susceptibility tests were considered to identify antibiotic resistance determinants (Tables  3  and 4 ). Preliminary assays for cloning certain genes were also performed in order to check their capacity to confer antibiotic resistance (see below).
Conclusion We studied the genomes of C. bolteae and C. clostridioforme, two species of the complex Clostridioforme, which can behave either as members of the human microbiota or as opportunistic pathogens. We compared the genomes of six clinical isolates of C. bolteae and 6 strains of C. clostridioforme with available genome sequences in laboratory and international databanks. These data were used as a basis to reveal differences in functional patterns between the two species. Among them, differences in flagella coding genes and butyrate pathways can potentially influence host-gut microbiota interactions. The patterns of resistance genes in the genomes were also of peculiar interest: (i) these bacterial species harbour specific and indigenous putative resistance genes, which included ABC transporters, antibiotic modifying enzymes, rRNA methyltransferases, (ii) Other resistance genes were acquired as shown by their location within mobile elements in the genomes studied.
Genomic DNA for sequencing was prepared from cells by lysis in lysozyme, incubation in proteinase K/SDS, followed by a standard phenol/chloroform extraction procedure. Prior to library preparation DNA quality was assessed by Nanodrop analysis (Thermo Scientific). Molecular identification of strains was performed by sequencing a 1483-bp PCR fragment from 16S rRNA using universal primers B27F (5'-AGAGTTTGATCCTGGCT CAG) and U1492R (5'-GGTTACCTTGTTACGACTT)  [34]. The taxonomic assignment of sequences was checked on the RDP classifier of the Ribosomal Database Project v11. 1  [35]. Closely related 16S rRNA sequences found in the database were aligned with the newly determined sequences and a phylogenetic tree was re-constructed according to the neighbor-joining method of the Phylip package  [36].
Annotation of genome sequences and ortholog groups Automated genome annotation was generated at the Broad Institute. Additional information was obtained through a proteome analysis pipeline including protein domains search by RPS-Blast with the NCBI Conserved Domain Database  [37]  and, manual annotation. To improve annotation of the genomes, we determined ortholog groups accross our dataset by using the singlelinkage algorithm of BlastClust (NCBI Blast package) at a high stringency level (S90-L0. 9). If necessary, a protein sequence was (re)annotated with the ortholog group whose average Blast similarity was highest. In addition, CDSs named according to their position on contigs (ordered locus number) allowed to identify conserved synteny between genomes. Annotation of clusters was also conducted by homology search against the Clusters of Orthologous Groups (COG) protein database  [38].
Detection of putative resistance genes Antibiotic resistance genes were searched for by using BLASTp at various stringency levels against the Antibiotic Resistance Genes Database (ARDB) on a local machine  [39]. Manual sorting of similarities and positive matches of the predicted protein sequences permitted to select manually the closely related AR proteins, and to assign functional annotation. Putative extrachromosomal elements (e. g. , ICEs or Transposons) were detected in the genomes of C. clostridioforme and C. bolteae by using BLASTp through the ICEberg database  [40]  upgraded with a personal collection of mobile elements from Firmicutes. A visual inspection of microsynteny (small scale of synteny) was also performed in regions surrounding resistance genes in order to detect mobile elements (e.
Accession to our set of genomic sequences of Clostridium spp in public databasesAll the genomes sequenced are available on the Broad Institute website (http://www. broadinstitute. org/data-software-and-tools) (Genbank Bioprojects : PRJNA64845, PRJNA64847, PRJNA64849, PRJNA64851, PRJNA64853, PRJNA64857, PRJNA659, PRJNA64861, PRJNA64863, PRJNA64865, PRJNA64867, PRJNA64869). The genomes of C. beijerinckii NCIMB 8052 (Clostridium sensu stricto), C. saccharolyticum WM1, C. bolteae BAA613 were retrieved from the PATRIC website (Genbank acc number: CP000721, CP002109; Bioproject: PRJNA18165). The genomes of Clostridium butyricum 60E. 3. 1, Clostridium colicanis 209318 (Clostridium sensu stricto), Clostridium bolteae WAL-14578, C. clostridioforme CM201. 1, Clostridium clostridioforme 2149FAA. 1, C.
clostridioforme WAL-7855 (Clostridium cluster XIVa) and, C. innocuum 6_1_30. 1 (Clostridium cluster XVIII) were selected from the Broad Institute (Genbank Bioprojects: PRJNA64855, PRJNA64873, PRJNA46383, PRJNA64871, PRJNA46389, PRJNA46391, PRJNA39359, respectively).
Acknowledgements The authors thank  Patrice Courvalin  for the opportunity to sequence the 12 genomes of C. bolteae and C. clostridioforme at the  Broad Institute , for helpful discussions and for comments regarding this manuscript. The authors thank all collaborators involved in the sequencing project. This research work has been funded in part with Federal funds from the  National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services , under Contract No. : HHSN272200900018C  and Grant  U54HG0049  to the  Broad Institute.
Emissions from fossil fuels and industry and their uncertainty The calculation of global and national CO 2 emissions from fossil fuels, including gas flaring and cement production (E FF ), relies primarily on energy consumption data, specifically data on hydrocarbon fuels, collated and archived by several organisations  (Andres et al. , 2012). These include the Carbon Dioxide Information Analysis Center (CDIAC), the International Energy Agency (IEA), the United Nations (UN), the United States Department of Energy (DoE) Energy Information Administration (EIA), and more recently also the Planbureau voor de Leefomgeving (PBL) Netherlands Environmental Assessment Agency. Where available, we use national emissions estimated by the countries themselves and reported to the UNFCCC for the period 1990-2014 (40 countries).
We propagate these new estimates for China through to the global total to ensure consistency. Our emission totals for the UNFCCC-reporting countries were recorded as in the UNFCCC submissions, which have a slightly larger system boundary than CDIAC. Additional emissions come from carbonates other than in cement manufacture, and thus UNFCCC totals will be slightly higher than CDIAC totals in general, although there are multiple sources of differences. We use the CDIAC method to report emissions by fuel type (e. g. all coal oxidation is reported under "coal", regardless of whether oxidation results from combustion as an energy source), which differs slightly from UN-FCCC. For the most recent 1-2 years when the UNFCCC estimates (1 year) and UN statistics (2 years) used by CDIAC are not yet available, we generated preliminary estimates based on the BP annual energy review by applying the growth rates of energy consumption (coal, oil, gas) for 2015 to the national and global emissions from the UN national data in 2014, and for 2014 and 2015 to the CDIAC national and global emissions in 2013.
For 2014 and 2015 we use estimates of cement production made by the USGS for the top 18 countries (representing 85 % of global production; USGS, 2016b), while for all other countries we use the 2013 values (zero growth). Some fraction of the CaO and MgO in cement is returned to the carbonate form during cement weathering, but this is neglected here. Estimates of emissions from gas flaring by CDIAC are calculated in a similar manner to those from solid, liquid, and gaseous fuels and rely on the UN energy statistics to supply the amount of flared or vented fuel. For the most recent 1-2 emission years, flaring is assumed constant from the most recent available year of data (2014 for countries that report to the UNFCCC, and 2013 for the remainder). 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 includes 255 countries and regions. This list includes countries that no longer exist, such as the USSR and East Pakistan. For the carbon budget, we reduce the list to 219 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 territories first appeared. The disaggregated estimates should be treated with care when examining countries' emissions trends prior to their disaggregation. For the most recent years, 2014 and 2015, 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.
, 2011a). Our analysis is based on the eco-nomic and trade data from the Global Trade and Analysis Project (GTAP;  Narayanan et al. , 2015) , and we make detailed estimates for the years 1997  (GTAP version 5), 2001 (GTAP6), and 2004 , 2007 , and 2011 (GTAP9. 1) (using the methodology of  Peters et al. , 2011b). The results cover 57 sectors and up to 141 countries and regions. The detailed results are then extended into an annual time series from 1990 to the latest year of the GDP data (2014 in this budget), using GDP data by expenditure in current exchange rate of US dollars (USD; from the UN National Accounts Main Aggregates Database; UN, 2015c) and time series of trade data from GTAP (based on the methodology in  Peters et al. , 2011b). 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.
CO 2 emissions from land use, land-use change, and forestry (E LUC ) Land-use-change emissions reported here (E LUC ) include CO 2 fluxes from deforestation, afforestation, logging (forest degradation and harvest activity), shifting cultivation (cycle of cutting forest for agriculture and then abandoning), and regrowth of forests following wood harvest or abandonment of agriculture. Only some land management activities are included in our land-use-change emissions estimates (Table  5 ). Some of these activities lead to emissions of CO 2 to the atmosphere, while others lead to CO 2 sinks. E LUC is the net sum of all anthropogenic activities considered. Our annual estimate for 1959-2010 is from a bookkeeping method (Sect. 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 are only available at intervals of 5 years.
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 (GFED4; accessed July 2016) described in van der Werf et al. (  2010 ) but with updated burned area  (Giglio et al. , 2013)     a See also  Goll et al. (2015). b Joint UK Land Environment Simulator. c See also  Best et al. (2011). d Lund-Potsdam-Jena. e Compared to published version, decreased LPJ wood harvest efficiency so that 50 % of biomass was removed off-site compared to 85 % used in the 2012 budget. Residue management of managed grasslands increased so that 100 % of harvested grass enters the litter pool. f Compared to published version: changed several model parameters, due to new tuning with multiple observational constraints.
Hence, the reduced ensemble of DGVMs that can simulate the LUC flux from the HYDE data set only. The HYDE data are 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). The HYDE land-use-change data set does not indicate whether land-use changes occur on forested or non-forested land; it only provides the changes in agricultural areas. Hence, it is 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 nonagricultural 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.
Growth The uncertainty around the annual growth rate based on the multiple stations data set ranges between 0. 11 and 0. 72 GtC yr -1 , with a mean of 0. 61 GtC yr -1 for 1959-1979 and 0. 19 GtC yr -1 for 1980-2015, when a larger set of stations were available  (Dlugokencky and Tans, 2016). It is based on the number of available stations, and thus takes into account both the measurement errors and data gaps at each station. This uncertainty is larger than the uncertainty of ±0. 1 GtC yr -1 reported for decadal mean growth rate by the IPCC because errors in annual growth rate are strongly anti-correlated in consecutive years leading to smaller errors for longer timescales. The decadal change is computed from the difference in concentration 10 years apart based on a measurement error of 0. 35 ppm.
This error is based on offsets between NOAA/ESRL measurements and those of the World Meteorological Organization World Data Centre for Greenhouse Gases (NOAA/ESRL, 2015) for the start and end points (the decadal change uncertainty is the 2(0. 35 ppm) 2 (10 yr) -1 assuming that each yearly measurement error is independent). This uncertainty is also used in Table  8. The contribution of anthropogenic CO and CH 4 is neglected from the global carbon budget (see Sect. 2. 7. 1). We assign a high confidence to the annual estimates of G ATM because they are based on direct measurements from multiple and consistent instruments and stations distributed around the world  (Ballantyne et al. , 2012). In order to estimate the total carbon accumulated in the atmosphere since 1750 or 1870, we use an atmospheric CO 2 concentration of 277 ± 3 or 288 ± 3 ppm, respectively, based on a cubic spline fit to ice core data  (Joos and Spahni, 2008).
Data availability The data presented here are made available in the belief that their wide dissemination will lead to greater understanding and new scientific insights into how the carbon cycle works, how humans are altering it, and how we can mitigate the resulting human-driven climate change. The free availability of these data does not constitute permission for publication of the data. For research projects, if the data are essential to the work, or if an important result or conclusion depends on the data, co-authorship may need to be considered. Full contact details and information on how to cite the data are given at the top of each page in the accompanying database and are summarised in Table  2. The accompanying database includes two Excel files organised in the following spreadsheets (accessible with the free viewer http://www.
We demonstrate that the RTBF, even if it can be criticized on many other aspects (such as the privatisation of quasi-judicial decision, the lack of process transparency, and the reinforcement of a quasi-monopolistic hegemony of the search engine on freedom of information), the ruling as such is not likely to prevent preservation. The recent legal provision of the right to erase should not conflict with archiving, statistical, scientific, and historical research purposes in order to balance privacy with the public interest. We contend that the RTBF name is misleading, as the content is not removed from the online space, but merely delisted from search results. Thus, the digital resource does not disappear, it remains available on the web and can be further analysed and disseminated. In a first socio-legal part (part 2), after a presentation of core legal concepts of privacy, data protection and freedom of expression, we analyse the case and the right to be forgotten, and look deeper into the controversies that have been generated by the decision.