dataesr/scientific-paragraphs-categorization huggingface dataset:
It is characterized by the fact that the person (speaker/writer) has only partial access to the word s/he is looking for. The typically lacking parts are phonological (syllables, phonemes). Since all information except this last one seems to be available, and since this is the one preceding articulation, we say: the word is stuck on the tip of the tongue. 2 Think of Roget's Thesaurus  (Roget, 1852) , WordNet  (Fellbaum, 1998; Miller et al. , 1990 ), Longman's Language Activator  (Summers, 1993) , the Oxford Reverse Dictionary  (Edmonds, 1999)  or OneLook which combines a dictionary, WordNet, and an encyclopedia, Wikipedia (http://onelook. com/reverse-dictionary. shtml). This being said, to build a dictionary for the language producer, certain provisions must be made, and it is easy to understand why.
Given Words Target Word gin, drink, scotch, bottle, soda whisky wheel, driver, bus, drive, lorry car neck, animal, zoo, long, tall giraffe holiday, work, sun, summer, abroad vacation home, garden, door, boat, chimney house blue, cloud, stars, night, high sky Table  1. Lists of given words together with their targets. We provided a training set of 2000 sets of five input words (multiword stimuli), together with the expected target words (associative responses). The way how the datasets were produced will be described in the next section. The participants had about five weeks to train their systems on this data. After the training phase, we released a test set containing another 2000 sets of five input words, but without providing the expected target words. The participants were given five days to run their systems on the test data, 3  with the goal of predicting the target words.
For each system, we compared the results to the expected target words and computed an accuracy based on the number of exact string matches (but without taking capitalization into account). The participants were invited to submit a paper describing their approach and their results. For the participating systems, we distinguished two categories: 1) Unrestricted systems. They could use any kind of data to compute their results. 2) Restricted systems based on ukWaC: These systems were only allowed to draw on the freely available ukWaC corpus  (Ferraresi et al. , 2008)   4  in order to extract information on word associations. The ukWaC corpus comprises about 2 billion words of web texts and provides also lemma and part-of-speech information. Participants could compete in either category or in both.
Training and test data sets and evaluation procedure The training and the test data sets were both derived from the Edinburgh Associative Thesaurus (EAT;  Kiss et al. , 1973). The EAT lists for each of 8400 stimulus words up to 100 associative responses as obtained from test persons who were asked to produce the word coming spontaneously to their mind. As the EAT uses uppercase characters only, and as this might not suit everybody's needs, we decided to modify its capitalization. For this purpose, for each word occurring in the EAT, we looked up which form of capitalization showed the highest occurrence frequency in the British National Corpus  (Burnard & Aston, 1998). By this form we replaced the respective word. E. g. DOOR was replaced by door, and GOD was replaced by God. This way we hoped to come close to what might have been produced during compilation of the EAT if case distinctions had been taken into account.
5 Since this method is not perfect, e. g. words often occurring in sentence initial position might be falsely capitalized, we did some manual checking, but cannot claim to have achieved perfection. Next, for each stimulus word, only the top five associations (i. e. the associations produced by the largest number of test person) were retained, and all other associations were discarded. The decision to keep only a small number of associations was motivated by the results of  Rapp (2013)  which indicate that associations produced by very few test persons tend to be of arbitrary nature. We also wanted to avoid unnecessary complications, which is why we decided on a fixed number, although the exact choice of five is of course somewhat arbitrary. From the remaining dataset we removed all items which contained non-alphabetical characters.
Participating systems and results Altogether 15 teams expressed their interest to participate in the shared task. Of these, ten teams actually submitted results, of which one (BRNO) participated in both tracks (ukWaC and unrestricted), and another (SAAR) provided two solutions for the unrestricted track. The teams who submitted results are listed in Table  5 , where each team is assigned a short Team ID which is derived from the institution names. In Table  6  for each team we make an attempt to give short characterizations of the approaches and the resources used. Most approaches are variants of analyzing word co-occurrence statistics as derived from large text corpora. Several teams, among them the best performing ones, use for this purpose the open source tool Word2Vec which provides two neural network-based model architectures for computing continuous vector representations of words from very large data sets  (Mikolov, 2013a; Mikolov, 2013b).
In contrast, the RACAI team uses WordNet relation chains, a method which makes absolutely sense, but seems to severely suffer from data sparseness issues (i. e. there are much fewer WordNet relations between words than there are non-random word co-occurrences within large corpora). This finding is confirmed by the BRNO and UBC teams who tried out both approaches (corpus-based and WordNetbased) and came to the conclusion that the corpus-based approach performed considerably better. Let us emphasize that we consider this type of findings a valuable output of the shared task and therefore are very grateful to the teams who pursued the WordNet-based approach that they shared these results although they were all well aware that, despite excellent scientific work, the respective performance figures were not very competitive.
Acknowledgments This work was supported by a  Marie Curie Intra European Fellowship  within the 7th European Community Framework Programme. We would like to thank  George R. Kiss  and colleagues for creating the  Edinburgh Associative Thesaurus , and  Michael Wilson  for making it publicly available. Many thanks also to  Adriano Ferraresi  and colleagues for providing the ukWaC corpus, and to the participants of the shared task for their contributions and comments, as well as for the pleasant cooperation.
, 2007; Beddows et al. , 2009; Harrison et al. , 2011). In Germany a similar initiative, established in 2008 by the German Environment Agency and known as the German Ultrafine Aerosol Network (GUAN), brings together several German institutes with an interest in sub-micrometre aerosol properties  (Birmili et al. , 2009). At the European level, the EUSAAR (European Supersites for Atmospheric Aerosol Research) project of the Sixth Framework Programme of the European Commission contributed to the development of a harmonised and quality-controlled network of measurements  (Philippin et al. , 2009; Asmi et al. , 2011). The EUSAAR project coordinated 24 European sites measuring aerosol chemical, physical and optical properties following a standardised protocol of instrument maintenance, measurement procedures and data delivery using a common format to a single database  (Wiedensohler et al.
, 2012). Recently, the EUSAAR project activities continued within the ACTRIS project  (Philippin et al. , 2009). Asmi et al. (2011)  presented a detailed overview of the sites and seasonally disaggregated size distributions collected at the EUSAAR and GUAN stations for the dry diameters between 30 and 500 nm for the years 2008 and 2009. The sites used to collect data are given in Table  1  and the reader is referred to  Asmi et al. (2011)  for a full account and description. It is important to note that all these sites are considered as remote or rural regional monitoring sites. Data were collected using either Differential Mobility Particle Sizer (DMPS) or Scanning Mobility Particle Sizer (SMPS) instruments and then harmonised into a single data set. Briefly,  Asmi et al. (2011)  showed that the aerosol concentrations in Europe are highly variable spatially and divide their description into nine areas: central European; Mountain; Arctic; Balkans; Nordic, Baltic, north Italian; Mediterranean; western European (Fig.
Methodology Although the instruments within the 24-site network of SMPS/DMPS devices used several different size ranges, all the data collected were harmonised into one large matrix by interpolating the data onto a common size bin scale; 121 size bins spanning 1 to 1000 nm with 40 channels per decade were used. Not all instruments shared the same size range or used the same size bins, hence blank lower and upper columns were trimmed from this matrix until every element had a value. In all, the reduced matrix had 54 size bins (17. 8-375. 8 nm) and 117 000 hourly particle size spectra (given the capture rates of the instruments across the EUSAAR network in 2008/2009). The data are extracted from the EBAS database (http: //ebas. nilu. no), located at NILU (the Norwegian Institute for Air Research), which is a database hosting data for projects and programmes such as the European Monitoring and Evaluation Programme (EMEP), the Global Atmospheric Watch -World Data Centre for Aerosols (GAW-WDCA) and the European Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS) network.
Hourly data For the analysis on an hourly resolution, we successfully tested the k-means functions provided in the CRAN R-Package BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies). This package is used when dealing with very large data sets and it does not require that the data can fit in physical memory. BIRCH is an implementation of the algorithms described in  Zhang et al. (1997) , and provides functions for creating clustering feature trees, along with algorithms for applying k-means clustering (although limited to 30 size bins). This resulted in a nine-cluster result optimised using an RSS value (i. e. the total residual sum of squares of the clustering). However, the diurnal shifts in modal diameter are lost when clustering hourly spectra (see Figs. S1 and S2 in the Supplement).
S17 and S18 in the Supplement). However, the diurnal patterns of the DSWF were distinct, reaching a maximum of 400 W m -2 in the daytime. There were patches of rain along the trajectory between ASP and WAL and between ZSF and CMN due to a weather front. As to be expected, the temperature increased from -10 • C at PAL to a daytime high of 20 • C at CMN and the RH dropped from 100 % to between 40 and 60 % at CMN (Figs. S17 and S18 in the Supplement). It should be noted that the final three stations are all sited at high altitude, which will have influenced the data (HPB 988 m; ZSF 2670 m and CMN 2165 m a. s. l. ) When classifying these, it is interesting that bimodal distributions are observed which are not clearly identified as belonging to any of our clusters until considering the lower percentile of the distributions -e.
Acknowledgements. The  National Centre for Atmospheric Science  is funded by the  UK Natural Environment Research Council. This work was also supported by the  European Union EUCAARI  (Contract Ref. 036833) and EUSAAR (Contract Ref. 026140) research projects. Thanks are also expressed to the  British Atmospheric Data Centre , which is part of the  NERC National Centre for Atmospheric Science (NCAS) , for providing access to calculated trajectories using data from the  European Centre for Medium-Range Weather Forecasts. Acknowledgements for the funding of data collection and harmonisation appear in Asmi et al. (2011) and in the interests of brevity are not repeated here. Edited by:  A. Petzold.
Ten distinct subgroups of plant trypanosomatids have been defined using the internal transcribed spacer region of the ribosomal RNA locus  [22]. Only Group H, encompassing the Latin American intraphloemic trypanosomatids responsible for severe wilts, can be distinguished both by rRNA markers as well as biological and serological properties  [19]. A full definition of the diversity of trypanosomatids within the overarching Phytomonas genus is still outstanding. The whole genome sequences of Trypanosoma cruzi, Trypanosoma brucei and Leishmania major were released in 2005  [28] [29] [30] [31]. Since then, the genomes of several additional trypanosomatids, including several pathogens of mammals, have been completed and described  [32] [33] [34] [35]. These databases have provided an essential platform for investigations of basic biology and mechanisms of pathogenesis and facilitated the exploration of novel therapies.
However, to date genome level analysis of Phytomonas spp. is limited. The biology of these parasites is reasonably well described  [11, 36] , but little information exists on their effective control by chemicals or, most critically, on their specific adaptations to the plant host and the mechanisms underpinning pathogenesis. Moreover, few genes are available in sequence databases, and little is known about genome size, chromosomal organization and ploidy  [37, 38]. We describe here the genome sequences of two plant trypanosomatids, one phloem-restricted pathogenic isolate from a diseased coconut from Guiana (HART1 from Group H) and the other a non-symptomatic latex isolate from Euphorbia (EM1 from Group D)  [22]. The comparison of these two plant parasite genomes with each other and with those of other trypanosomes reveals a common simplified genome organization for the plant trypanosomes.
General features of Phytomonas EM1 and HART1 genomes Recently, the molecular karyotype of several different latex plant symbiont-like (i. e. not associated with apparent pathology in the host) Phytomonas isolates were analyzed by pulsed-field gel electrophoresis (PFGE), showing 21 chromosomal bands for EM1 (group D)  [37]. Similar analysis performed on phloem-restricted trypanosomatids allowed the identification of 7 chromosomal bands for the Hartrot wilt pathogen isolate (HART1, Group H)  [38]. A systematic genome sequencing project of these two Phytomonas isolates was initiated, as they represent two distinct phenotypes in terms of impact on the host. Both EM1 and HART1 genomes were assembled using 106 454-technology and 0. 16 Sanger reads, together with deep coverage Illumina sequencing reads for correction of sequencing errors  [39]  (European Nucleotide Archive accession numbers CAVQ010000001-CAVQ010001400 for EM1 and CAVR010000001-CAVR010002560 for HART1; details in Text S1).
chromosomes  [40] , suggessting the existence of separate mechanisms for gene duplication and chromosome (scaffold) duplication in Phytomonas spp. Nonetheless, read depth reached values greater than twofold in some cases (scaffolds 24 and 25 in Figure  S1  A; scaffolds 13 and 22 in Figure  S1 B ), probably indicating, as for other parasite genomes, aneusomy of certain chromosomal regions (Figure  S3  and S4)   [127] , together with the top BLAST hits retrieved from HART1 and EM1 genome sequence data using HSP90 as a query. Orthology was established by reverse BLAST into the non-redundant database. Trees were constructed by multiple sequence alignment followed by trimming of the N-and C-termini (the EM1 sequence is truncated), and reconstructions by Mr Bayes and PhyML. Statistical support is shown for all nodes as Mr Bayes/PhyML posterior probabilities/bootstraps, respectively.
The two Phytomonas isolates analysed here are colored in red, and the N. gruberi sequence, included as an outgroup, is in gray. Note that branch lengths indicate that EM1 and HART1 are of similar divergence as T. brucei brucei versus T. congolense or L. infantum versus L. mexicana. EM1 is more closely related to P. serpens than to HART1 based on this dataset. doi:10. 1371/journal. pgen. 1004007. g001 [40, 41]. Possible aneusomy was already envisaged for the Phytomonas HART1 isolate after study of its molecular karyotype  [38]. This increase in read depth is not likely due to the amplification of specific regions of the scaffolds, since read depth was constant along the whole of both the disomic and tetrasomic regions (Figure  S5 ). Both assemblies were annotated using a combination of evidence (Table  S2 ; for details, see Text S1), with the major features of the genome annotation presented in Figure  1  A.
Proteins involved in kDNA replication, kRNA editing, modification and translation The topological complexity of the kDNA network has fascinated replication specialists for decades. The process is not fully understood, but many of the players have been identified. In the model flagellate T. brucei, the machinery is extremely complex, requiring the combined activity of several mitochondrial DNA polymerases, ligases, endonucleases, helicases and topoisomerases  [72]. Using a database of 26 genes encoding the kDNA replication machinery of T. brucei, all orthologs have been identified in the Phytomonas EM1 and HART1 isolates. The transcripts of many maxicircle genes undergo RNA editing in order to be translatable on mitochondrial ribosomes. Editing and processing of these mRNAs require the participation of several dozen proteins.
This is due to the high divergence between the trypanosomatid sequences and the one used by SignalP for learning, mostly from fungi, animals, plants or bacteria origin. In the set of putative secreted proteins, we looked for proteins involved in plant carbohydrate degradation. One protein having a glycoside hydrolase family 31 domain was present in both HART1 and EM1 isolates, but the EST data did not show any expression of the two corresponding genes. We also found a secreted protein in HART1 (GSHART1T00001406001) coding for glycosyl hydrolase family 32 that corresponded to one of the betafructofuranosidases (see Metabolism of HART1 and EM1 section); other beta-fructofuranosidases harbored a signal peptide and GPI anchor. We did not identify any secreted proteins that were supported by expression data and likely to be involved in plant cell wall degradation.
The cluster of five genes was created in the scaffold 1 of HART1 by four successive tandem duplications. The presence of a signal peptide in the AP from EM1, the AP in scaffold 5 and one AP in the cluster of scaffold 1 let us speculate about the presence of a signal peptide in the other four APs, but their sequences were too short to detect it. The scaffold gaps between the five AP genes may correspond to repeated elements that may have mediated the AP tandem gene duplication. The EST data provided evidence for the expression of the five AP genes which comprise the AP cluster in the Phytomonas HART1 isolate (Figure  S23 ), suggesting that, similarly to the function of the AP family in the fungi Candida and Bothrytis  [101, 104] , the Phytomonas HART1 AP gene cluster could be involved in virulence, an example of convergent evolution between distant organisms.
Identification of candidate horizontal gene transfers Phytomonas proteins were used against the protein nr database (blastx,  [117] ), with the parameters ''-f 100 -X 100 -e 0. 00001 -E 2 -W 5'', and the best hits were retained using the following criteria: only BLAST scores greater than 90% of the best score outside kinetoplastids (so that horizontal gene transfers shared between kinetoplastids could be detected) and above 100 were retained. Then, the proteins with all their best hits in bacteria or archaea were retained as candidates to have arisen from bacterial/archeal horizontal gene transfers. All the candidates were then manually inspected, which provided 87 final candidate HGT genes, 80 of which have orthologs in other trypanosomatids, and 8 have no orthologs in Leishmania spp. nor Trypanosoma spp.
Analysis of the Phytomonas secretome Proteins with a signal peptide were detected with SignalP version 3. 0  [122] , transmembrane domains were detected with TMHMM 2. 0  [123]  and GPI anchors with KOHGPI version 1. 5 (http://gpi. unibe. ch/). Proteins harboring a signal peptide, not containing transmembrane domains nor GPI anchors were considered to be secreted by Phytomonas, and their annotation was performed using BLASTp against the non-redundant NCBI database, Interproscan and Gene Ontology. We retrieved aspartic proteases from others clades (amoebae, plants, chromalveolates, fungi and animals) using the Phytomonas aspartic proteases amino acid sequences as queries with BLASTp on the NCBI nr database  [121]. Phylogenetic analysis was executed on the Phylogeny. fr platform  [124]  as described in  [125] , with the parameters ''minimum length of a block after gap cleaning: 5, no gap positions were allowed in the final alignment, all segments with contiguous non conserved positions bigger than 8 were rejected, axis the position of the EM1/HART1 gene on the EM1/HART1 assembly, and on the y axis the position of the Tv gene on the Tv assembly.
(XLS) Table  S7  Transposable elements in EM1 and HART1 Phytomonas genomes. a, number of amino acids contained in the multifunctional protein encoded by the consensus sequence of autonomous and active retroposons; b, autonomous retroposons (''Auto'') potentially code for a protein responsible for their retrotransposition. Retroposons are considered active when bioinformatics analyses suggest recent retrotransposition events for most of the elements in the family; c, copy number per haploid genome. ; d, not determined due to the high sequence heterogeneity; e, non-coding retroposons; f, the copy number of each retroposon in the 41. 8 Mb dataset (the size of the haploid genome is not known); g, the copy number of each retroposon in the 47. 7 Mb dataset (the size of the haploid genome is not known); h, number of copies in the assembled sequences.
Statistical support is shown for all nodes as Mr Bayes/PhyML posterior probabilities/bootstraps, respectively. The two Phytomonas isolates analysed here are colored in red, and the N. gruberi sequence, included as an outgroup, is in gray. Note that branch lengths indicate that EM1 and HART1 are of similar divergence as T. brucei brucei versus T. congolense or L. infantum versus L. mexicana. EM1 is more closely related to P. serpens than to HART1 based on this dataset. doi:10. 1371/journal. pgen. 1004007. g001.
Acknowledgments We are grateful to  Patrick Bastien  and  Michel Pages  ( CNRS 5093/IRD 224/UniversiteMontpellier 1 & 2 ,  UMR MIVEGEC) , and  Theo Baltz  ( UMR-CNRS 5234 ,  UniversiteVictor Segalen Bordeaux 2)  for their strong support in the building of this project and useful discussions. We specially acknowledge  Najib El Sayed  ( Department of Cell Biology and Molecular Genetics, University of Maryland ) for his involvement in the elaboration of the project. We thank  Ophelie Dagail  for her technical assistance with in vitro cultures and DNA preparations. We are grateful to  Eric Pelletier  ( CEA, IG, Genoscope ) for fruitful discussions and technical support for the chromosome read depth analysis and  Amanda O'Reilly  ( Department of Pathology, University of Cambridge ) for generating the HSP90 tree.
This project, '' SEQTRYPLANT -Obtaining the full sequence of two plant trypanosomatids ,'' was funded by  ANR -Agence Nationale de la Recherche , grant  ANR-08-GENM 020-001  and  CEA. TCH 's group was supported by the  Medical Research Council  (grant number  0700127  to JCM and  New Investigator Research Grant  ( GO900239 ). The  Wellcome Trust   Centre for Molecular Parasitology  is supported by core funding from the  Wellcome Trust  [ 085349/Z/08/Z ]. FB was supported by the  Centre National de la Recherche Scientifique (CNRS) , the  UniversiteBordeaux Segalen. MCF's group is grateful to camGRID for computing resources and  Amanda O'Reilly  for informatics support. This work was supported in part by the  Wellcome Trust. Work of RD's laboratory was funded through a  Barbara and Sanford Orkin/Georgia Research Alliance Endowment Fund , and  NIH  grant  AI068467.