dataesr/scientific-paragraphs-categorization huggingface dataset:
In China, particularly high emissions are originating from industry (62 %) and residential (27 %), the latter also associated with the high usage of solvents in paints. In Brazil, particularly high usage of biogasoline is present resulting in a 52 % NMVOC contribution of the transport sector. Also the production of charcoal is emitting strongly NMVOC and the world's top three emitters (IEA, 2013) are Brazil, Thailand 10 and Kenya, which explains that their industry sector is contributing to the NMVOC total with, respectively, 35, 37 and 80 % in 2010. NMVOC speciation is not provided by the HTAP_v2. 2 emission database; however, TNO has produced a breakdown into 23 NMVOC species, which has been used for the RETRO project and the RCP scenarios of IPCC AR5. Recommendations for the NMVOC splits are given on the HTAP wiki site http://iek8wikis.
Per capita emissions To compare emissions from worldwide countries characterized by different degrees of development and numbers of inhabitants, per capita emissions were calculated. Countryspecific per capita total emissions are given in Table  S3. 1 of Annex III in the Supplement. In Table  2b  we compare for the world's top six CO 2 emitters, China, USA, India, Russia, Japan and Germany the per capita air pollutant emissions while making the link with the country's activity level and level of clean technologies development. Country total population data were obtained from the United Nations Population Division  (UNPD, 2013). This approach allocates the emissions from industrial production to a country without taking into account exports. No life cycle assessment of products at the point of consumption is considered here.
Per GDP emissions Another indicator of emission intensity of a country is the ratio of emissions and gross domestic product (GDP) in USD, in constant purchasing power parity (PPP), as given in Table  S3. 2 of Annex III and shown in Fig. 3b. The GDP 2010 data for the different countries were obtained from World Bank (2014) and IMF (2014). This indicator is much more uncertain than the per capita emissions because the GDP is subject to heterogeneity (by the different economic activities), to heteroskedasticity (by time-dependent inflation and currency exchange rates) and to incompleteness (by the not officially reported activities). It is not recommended to use this per unit of GDP emissions indicator for relatively small countries with substantial service sectors (e. g. Luxembourg). For 2010 Fig.
Here we compare the ECLIPSEv5 emission inventory for 2010 with the HTAP_v2. 2 2010 data, in order to evaluate how close the reference point is to the "officially accepted" regional inventories of HTAP_v2. 2. At the global level, a relatively good agreement is found with small relative emis-sion differences (ECLIPSEv5 -HTAPv2. 2)/HTAPv2. 2 for the aggregated sectors in 2010. It should be noted that the GAINS data set, another bottom inventory, can not be considered an external independent source of verification, because similar information on emission factors and reductions for certain technologies have been applied in the TNO-EMEP, MIX-Asia and EDGARv4. 3 data sets. The relative difference for NO x and CO is only -4 % and +5 %, respectively. For SO 2 a larger difference of -8 % reflects the recent important S-reductions for the non-ferrous metal smelters in ECLIP-SEv5  (Klimont et al.
Geo-spatial consistency is lower in the HTAP_v2. 2 database than if the national totals would have been spatially redistributed with one harmonised spatial proxy data set. It should be also noted that derivation of country totals from the 0. 1 • × 0. 1 • emission grid maps (as e. g. done in the ECCAD system) is only valid if the countryspecific total is larger than 0. 2 % of each of the totals of the neighbouring countries. Otherwise the derived countryspecific sector total can be 50 % larger than the bottom-up one, mainly in the energy sector with many point sources which are typically located on waterways or coastal areas and as such in cross border cells. Table  S1. 3 illustrates the deviations of derived country-specific sector totals to the bottom-up ones for the Asian region. The latter caused derived sector totals for Kyrgyzstan, Tajikistan, Afghanistan, Laos, Myanmar, Bangladesh, which deviated with 1 order of magnitude from the bottom-up totals.
However, the relatively small differences for China (≤ 5 %), India (≤ 3 % for all except for SO 2 from energy where it is 14 %), Indonesia (≤ 7 %) and Thailand (≤ 12. 5 %), Japan (≤ 16. 0 %) and South Korea (≤ 17. 3 %) show a good agreement for the top six Asian emitters. Another type of inconsistency in mass balance at grid cell level occurs when for the same region the data sources providing the emission grid maps for PM 10 and PM 2. 5 or for PM 2. 5 and BC / OC are different. Already the application of different spatial proxy data sets (e. g. with and without point sources) result in an inconsistent allocation of multi-pollutant sources to different grid cells. This was another reason not to use the PM grid maps of EMEP, as no BC and OC speciation is available from the same EMEP data source.
Conclusions and recommendations This paper describes the HTAP global air pollutant reference emission inventory for 2010, which is composed of latest available data from regional inventory compilers. It assures a consistent input for both regional and global modelling as required by the HTAP modelling exercise. The HTAP_v2. 2 emission database makes use of consolidated estimates of official and latest available regional information with air pollutant grid maps from US EPA and Environment Canada for North America, EMEP-TNO for Europe, MIX for Asia, and the EDGARv4. 3 database for the rest of the world. The mosaic of grid maps provides comprehensive local information on the emission of air pollutants, because it results from the collection of point sources and national emission grid maps at 0.
1 • (for some regions 0. 25 • ) resolution. Even though the HTAP_v2. 2 data set is not a self-consistent bottom-up database with activity data of consistent international statistics, with harmonized emission factors, and with global sets of spatial proxy data, it provides a unique set of emission grid maps with global coverage and high spatial resolution, including in particular important point sources. The compilation of implied emission factors and per capita emissions for the different world regions using multiple sources provides the regional and national emission inventory compilers with a valuable asset for comparison with their own data for cross checking and analysis which may lead to identification of future improvement options. This data set was prepared as emission input for the HTAP community of modellers and its preparation has involved outreach to global and regional climate and air quality modellers (collaborating also within the AQMEII and MICS-Asia modelling exercises).
The TF HTAP needed an emission inventory that was suitable for simultaneous and comparable modelling of air quality at the regional scale and at the global scale to deliver consistent policy support at both scales. The HTAP-v2. 2 emission inventory presented in this paper is tailor-made to allow the TF HTAP to fulfil its prime objectives and contribute to a common international understanding of global and regional air pollution and its influence on human health, vegetation and climate. The use of the HTAPv2. 2 inventory will substantially help to provide a basis for future international policies because it combines and is consistent with the inventories that are used for regional (EU, US Canada, China) policy analysis and support. refer to the link on the ECCAD data portal: http://eccad2. sedoo.
Acknowledgements. This research was partially funded by  EC-DG Environment  under  AA 070402/2007/47597/ MAR/C5  and  33075. S. Galmarini  ( JRC ) and  A. Zuber  ( DG Environment ) are thanked for the continuing support to the  HTAP  project. The authors would also like to thank all HTAP-modelers providing first feedback. Colleagues of the  ECCAD  project,  C. Granier ,  A. Mieville , of the  TNO-MACC  project and of  EMEP-MSCW  are gratefully acknowledged for help provided during the preparation of this work. Edited by:  G. Carmichael.
nomes selected on the basis of their 16S rRNA gene sequence proximity with M. neworleansense. The M. neworleansense genome was locally aligned 2 by 2 using a BLAT algorithm  (22, 23)  against each the selected genomes, and DDH values were estimated from a generalized linear model  (24)   (32). These data confirm M. neworleansense as a unique species more closely related to M. septicum in the M. fortuitum complex. Nucleotide sequence accession numbers. The M. neworleansense ATCC 49404 T genome sequence has been deposited at EMBL under the accession numbers CWKH01000001 to CWKH01000009.
Phylogenetic analysis Sequence data was retrieved from the NCBI RefSeq database (A. thaliana, C. merolae, C. reinhardtii, D. melanogaster, H. sapiens, N. gaditana, P. sojae, S. cerevisiae, S. pombe and X. laevis), through the JGI portal (P. tricornutum and T. pseuodonana) or the pico-PLAZA database (E. siliculosus, F. cylindrus, M. pusilla, M. sp. , O. tauri and O. lucimarinus)  [12, 52]. Multiple alignments based on CDK amino acid sequences were generated with MUSCLE  [53]  and then manually improved, yielding 226 amino acid positions (Additional file 6: Figure  S3 ). To define subclasses within the gene families, phylogenetic trees were built that included reference CDK sequences from animals, yeast, higher plants, stramenopiles and several algal species. MEGA5. 1 was used to construct the maximumlikelihood tree using the rtREV + G + I model.
Diatom culture conditions Phaeodactylum tricornutum (Pt1 8. 6; accession numbers CCAP 1055/1 and CCMP2561) was grown in f/2 medium without silica (f/2-Si)  [54]  made with autoclaved filtered sea water. For routine cultivation, cells were grown at 18-20°C in a 12-h light/12-h dark regime and 70-100 μmol photons m -2 s -1. Liquid cultures were shaken at 100 rpm. For biolistic transformation, P. tricornutum cells were grown on solid f/2-Si medium containing 1% Select agar (Sigma).
tricornutum genome database (http://genome. jgi-psf. org/Phatr2/Phatr2. home. html) to identify the putative interactor protein. To confirm the protein-protein interactions of interest, the respective prey and bait plasmids were cotransformed in the yeast strain PJ694-alpha, and the interactions were retested on medium lacking Leu, Trp and His and by X-Gal testing.
5 μM gene-specific primers (YFP_Fw: TGCTTCGCCCGCTAC CC and YFP_Rv: ATGTTGCCGTCCTCCTTGAAG; E2 F1_Fw: CCCTAAGCGGCGGATTTACG; E2F1_Rv: AAG CGACGAGCCAAGAAGAAGC; other primers see  [21] ). The cycling conditions were 10 min polymerase activation at 95°C and 45 cycles at 95°C for 10 s, 58°C for 15 s and 72°C for 15 s. Amplicon dissociation curves were recorded after cycle 45 by heating from 65°C to 95°C. Data were analyzed using the qbase + software package (Biogazelle) using the stably expressed EF1a and TubA as normalization genes  [57].
Acknowledgements The authors wish to thank  Annick Bleys  for help in preparing the manuscript. This work was supported by grants of the  Research Foundation Flanders  ( G. 0288. 13 )and  Ghent University  ( BOF  project  GOA 01G01715 ). MJJH acknowledges the  European Molecular Biology Organization (EMBO)  for a short-term fellowship ( ASTF 93-2011 ) to visit CB's group at  Institut de Biologie de l'Ecole Normale Supérieure (IBENS) Paris (France). CB additionally acknowledges the  ERC "Diatomite" Advanced Grant Award , and the  French Government 'Investissements d'Avenir' programmes MEMO LIFE  ( ANR-10-LABX-54 ) and  PSL* Research University  ( ANR-11-IDEX-0001-02 ).
LargestDev indicates which construction each lexeme is a distinctive collexeme of. Thanks to MDCA, one can determine the probability of each adjective's observed frequency given its expected frequency in each pattern. Only those adjectives whose absolute distinctiveness values are significant are kept, along with their respective observed frequencies in each of the eight quite/rather patterns. The obtained contingency table serves as the basis for the constitution of a multidimensional dataset of non-negative ratio-scaled data. This dataset contains supplementary frequency data regarding the syntax and the semantics of the quite/rather constructions. Supplementary columns provide the frequency of co-occurrence between each adjective type and the following variables: the syntactic position of the intensifier: pre-adjectival vs.
pre-determiner; the syntactic position/function of the adjective: attributive vs. predicative; the sum total for each intensifier: quite vs. rather; the text mode: spoken vs. written. Supplementary rows group the frequencies of adjectives according to 59 semantic classes listed in the appendix. The semantic classes are inspired from  Dixon & Aikhenvald (2004). Classes that match the data better are added in an ad hoc fashion. The dataset is then submitted to correspondence analysis (henceforth CA), an exploratory statistical technique that takes the frequencies of multi-way tables as input, then summarizes and visualizes distances between the variables  (Benzécri 1973 , 1984 , Greenacre 2007 , Glynn 2014). More precisely, CA uses the frequencies of the dataset to: compute a matrix based on χ 2 distance and determine the probability of global association between rows and columns; transpose the multidimensional distances to a two-dimensional plane that maps the correlations between the variables; each row and each column is thus represented as a point in the Euclidean space.
The graph is used to visualize relative distances between (a) intensifiers, (b) adjectives and their respective semantic classes, (c) syntactic information regarding how intensifiers and adjectives pattern together. The data are taken from the British National Corpus (World Edition), which consists of 100 467 090 words of spoken and written British English divided among over 4 054 texts. The spoken component contains approximately 10 million words, and consists of transcripts of spontaneous conversation and context-governed recording samples. The written component contains approximately 90 million words, and consists of samples of many kinds of text material (newspapers, fiction books, unpublished memoranda, etc. ) The whole corpus spreads across the period from 1960 to the early 1990s  (Burnard 2000).
Insofar as MDCA is not designed for the cross-comparison of variables, the structural limitations of Table  3  render further exploration uneasy. To assess the constructional near synonymy of quite and rather more deeply and more systematically, we need to (a) inspect more construction tokens, (b) describe the data with syntactic and semantic variables that cover several levels of granularity, and (c) determine those variables beforehand (i. e. not heuristically). If we do so, we face another problem: as we include more data and more variables, we need to build and inspect several tables at once. In such conditions, making cross-comparisons is tedious and unproductive. Therefore, rather than inspect and compare many collostruction-based frequency tables manually, I use correspondence analysis, a method that is well suited for the purpose of handling extensive datasets without compromising the complexity of the object of study.
This means that we should sort the data using categories that are broad enough to capture the general profiles of quite and rather and reflect the semantic and syntactic properties of each pattern. To determine the semantic profile of a construction, I examine its distinctive adjectives, grouped by semantic classes. To determine the syntactic profile of a construction, I look at whether the intensifier occurs in pre-adjectival or pre-determiner position and whether the adjective is predicative or attributive. Given that the profile of a construction may be sensitive to text mode, we should also inspect variation according to the written vs. spoken distinction. The above remarks translate into a dataset, sampled in Table  4. It contains: the raw frequencies of the 543 most distinctive adjectival collexemes (pbin > 5) of all 8 quite/rather constructions; -8 supplementary columns that correspond to the following variables: the syntactic position of the intensifier, the syntactic position/function of the adjective, the text mode, the sum total for each intensifier; -59 supplementary rows that group adjective frequencies according to semantic classes (positive value, negative value, atypicality, difference, etc.
) CA uses these frequencies to display the rows and columns as points on a twodimensional map. Interpreting the geometric positions of the points is a way of interpreting the similarities and differences between rows (i. e. adjectives), the similarities and differences between columns (i. e. quite and rather constructions), and the association between rows and columns. CA is performed in R (R Core Team 2015) with the CA function of the FactoMineR package  (Husson et al. 2009 ). χ 2 has a very high value (24 522. 65) and it is associated with the smallest possible pvalue, implying that there is a relationship between the row variables and the column variables. Yet, we should be wary of using the magnitude of the χ 2 value to quantify the effect of the correlation between variables, because this value depends on the sample size.
The total sample size (43 216) is small relative to the number of active cells in the table (543 x 8 = 4 344). This means that the table contains many cells with small or null values. We are far from meeting the assumptions of the χ 2 test, which stipulate that 80% of the sample size should be greater than 5, and the remaining 20% greater than 1. However, because CA is exploratory, it can be applied to tables even when the conditions of validity of the χ 2 statistic are not met  (Greenacre, 2007). Given that the pvalue is 0, the significance of the deviation of the table from independence is undeniable. In other words, the choice of adjectives and the choice of active variables are globally interdependent in the dataset. Central to CA is the concept of profile. To obtain the profile of a row, each row frequency is divided by its row total.
If we projected more adjectives, we would see that considerable clusters with nearsynonyms (compelling, massive, extensive, big, large, etc. ), which would corroborate our claim. In CA, supplementary rows and columns can also be plotted to help interpret the active rows and columns. As opposed to active elements (= white rows and columns in Table  4 ), supplementary elements do not contribute to the construction of the dimensions. They can still be positioned on the map, but only after Φ 2 has been calculated with the active elements. Given the number of rows in our dataset, plotting all 543 adjectives would make little sense, as the myriad of points would clutter the graph. To make these data points invisible while still being able to inspect their general profiles, the supplementary rows are projected instead (i.
e. the rows in grey in Table  4 ). These rows group the 543 adjectives into the 59 semantic classes described above. Each row cell is the sum of the frequencies of all construction tokens whose adjectives illustrate the semantic class. Because of their intermediate granularity, the supplementary rows are easier to interpret than the active rows. 8 supplementary columns (i. e. the columns in grey in Table  4 ) are also projected to: (a) see if the relationship between the syntax of the intensifiers and the syntax of the adjectives brings new insights on our dataset, (b) see if the mode (spoken vs. written) plays a role in the choice of a construction over another, and (c) summarize the general profiles of quite and rather to evaluate how close or distant they are. Figure  3  provides a superimposed representation of supplementary rows (in blue) and supplementary columns (in black).
As expected, Figure  2  confirms most of the tendencies revealed in Figure  1. The position of the supplementary column variables on the first dimension corroborates the broad divide between quite and rather -quite constructions clustering to the left, and rather constructions to the right. Because quite constructions are closer to the average column profile than rather constructions, the profile of rather stands out in our dataset. As will appear below, the choice of rather over quite does not depend on the mode (since both spoken and written are close to the average column profile) but on the meaning of the intensified adjective. Dimension 2 confirms the gradient of typicality inferred from Table  5. Typically, the intensifiers occur in pre-adjectival position, and the adjectives in the predicative position.
Since quite and rather can only occur in pre-determiner position when the adjective is attributive, we have reasons to expect a strong correlation between these two variables. Such is not the case as the attributive variable is also seemingly attracted to the pre-adjectival position (bottom left part of the plot). When quite and rather occur in pre-determiner position, we find no significant proximity with any of the semantic classes of adjectives. The extreme position of the pre-determiner variable on the vertical axis makes this syntactic pattern stand out in our dataset. This is due to the overrepresentation of <quite DET ADJ NP> in the pre-determiner context (see Figure  1 ). Regarding the semantic properties of quite and rather constructions, three configurations emerge. In the first configuration, each intensifier has an area of semantic specialization.
A more relevant option is to apprehend stylistic variation through more detailed modalities (fiction, news, conversation, etc. ). Such information is available separately from the BYU-BNC for each query result, but given the number of tokens involved, manual extraction is not a viable option. One interesting feature of exploratory methods is that one can look at the same dataset from different angles to make more tendencies appear. To this aim, one can even ignore irrelevant variables or add some variables one believes will prove relevant. To see how the pre-adjectival vs. pre-determiner alternation behaves in the light of more detailed contextual information, I imported contextual tags of the XML edition of the British National Corpus and augmented the dataset with three levels of contextual information instead of one for each observation: text mode, text type, and information regarding each text.
To remain within the strict limits of the pre-adjectival vs. pre-determiner alternation, I kept only those patterns that involve both alternants unquestionably, namely <a(n) quite/rather ADJ NP> and <quite/rather a(n) ADJ NP>. Following the same logic, I ignored the cases where the adjective was in predicative position. Finally, the 6 variables corresponding to the remaining syntactic patterns listed in Table  1  were gathered into only two levels ('pre-adjectival' and 'pre-determiner') within a unique variable ('construction'). I obtained a dataset of 3 086 observations sampled in Table  7. This dataset consists of five categorical variables: construction: 2 modalities (pre-determiner and pre-adjectival), intensifier: 2 modalities (quite and rather), text mode: 2 modalities (written and spoken), text type: 8 modalities (academic writing, non-academic writing, fiction, news, other published writing, unpublished writing, conversation, and other spoken), text information: 66 modalities (e.
g. S parliament, S interview, W pop lore, W newsp brdsht nat: editorial, etc. ), semantic classes: 59 modalities (e. g. adequacy, accuracy_suitability, difficulty_complexity, etc. ). The dataset was submitted to multiple correspondence analysis, henceforth MCA  (Greenacre & Blasius 2006; Le Roux 2010). As its name indicates, MCA is an extension of CA. It is used to analyze patterns of relationship between nominal variables. The general principles of CA outlined above also apply to MCA.