dataesr/scientific-paragraphs-categorization huggingface dataset:
59%) assigned as encoding hypothetical proteins. Moreover, 5,220 genes matched at least one sequence in the Clusters of Orthologous Groups (COG) database  (20, 21)  with BLASTp default parameters. This report illustrates the genomic variability within M. neoaurum (3), a feature on which to base further evaluations. Nucleotide sequence accession numbers. The M. neoaurum DSM 44074 T strain genome sequence has been deposited at EMBL under the accession numbers LK021337 to LK021346. The whole-genome shotgun master numbers are CCDR010000001 to CCDR010000045.
After measuring the total weight, the plastic cubes with the sediments were put in an oven at 80˚C and left for two days. Then, dry weight was measured and the sediments were subsequently ground in a mortar. Two grams of powdered samples were transferred to plastic tubes and sealed hermetically. The samples were left for more than two months to wait for secular equilibrium between  226  Ra and 222 Rn. Gamma-ray spectra were measured with a gamma-ray analysis system equipped by SEIKO EG&G consisting of an ORTTEC 12030 well type germanium detector and a MCA-7800 spectrum analyzer. The peak areas of 210 Pb (46. 5 keV) were calculated by Gaussian curve fitting using a KaleidaGraph 4. 1 software (Synergy Software, USA). Radionuclide activities were quantified and corrected for their respective counting efficiencies as determined by standard materials (CANMET DL-1a uranium-thorium ore).
e. , all chambers except the last one stained bright pink), and compared doubtful individuals to perfectly stained ones of the same species found in superficial sediment layers. Non-transparent agglutinated and miliolid taxa were broken on many occasions for inspection of the interior of the test. Most stained foraminifera were identified at species level (see Appendix 1 for census data including taxonomic lists). Because samples were preserved and sorted in ethanol, many soft-shelled foraminiferal species may have shrunk and become unrecognizable during picking. Thus, our counts probably underestimate the soft-shelled foraminiferal group. At each station, we calculated sample diversity (S) representing the number of taxa (identified at least to the genus level). As an information statistic index, we also calculated the Shannon Index (H') as described in Murray  12.
3 1+1. 2D * + 0. 0055(1-exp[-0. 020D * ])  (4)  with dimensionless grain size : D * = ρ s -ρ ( ) g ν 2 " # $ % & ' 1/3 D (5) We substituted variable silt-and sand-sized D into eqs. (  3 )-(5), and calculated u *cr for each grain size. Then a threshold curve between current velocities and grain sizes was delineated by substituting u *cr and D into eqs. (  1 ) and (2). In regard to Typhoon Songda waves, we calculated the water velocities using wave heights and periods recorded at the the Mutsu-Ogawara tide gauge (141°23'14''E and 40°55'35''N). Their data is available to the public by the NOWPHAS (Nationwide Ocean Wave information network for Ports and HArbourS) database system run by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT). The maximum wave height (11. 41 m) and period (9.
Microarray Analysis Raw background subtracted data was processed using Genespring V12. 6 (Agilent Technologies) and the following principles were applied to all analyses. After background subtraction low signal values (,10) were then set to a threshold of 10, log2 transformed and per chip normalised using 75 th percentile shift algorithm. Per-transcript normalisation was undertaken by normalisation to the median of a defined control group. Transcripts were then filtered out if they were not significantly (p,0. 01) different in intensity value compared to the background in at least 10% of all the samples. The resulting transcripts were then subjected to statistical filtering (either One-way ANOVA or 2-way ANOVA) with multiple testing correction (Benjamini-Hochberg p,0. 01), followed by a further filtering of transcripts by fold change (FC) in which transcripts were filtered if less than 1.
8 FC different between variables of interest. Expression heatmaps were generated within Genespring V12. 6. Heatmap clustering was undertaken using Differential distance metric and Wards linkage rule, unless otherwise stated. Media controls from 2 volunteers had evidence of activation of inflammatory genes by 3 hours of culture (Figure  S1A ). This activation persisted in all of the subsequent time points for these individuals (not shown) appears to be revealed in culture, is independent of the individual, and independent of the length of time in transport conditions (Figure  S1B ). These samples were excluded from the study. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE55375.
IFN Gene Expression is Dominant Following LPS but not Pam3CSK4 Stimulation Our data show that as early as 1 hour post stimulation IFN gene expression is seen to be upregulated following LPS but not Pam3CSK4 stimulation. This difference in IFN signalling was further emphasised by the k-means clustering, canonical pathway analysis and upstream analysis of both potential transcriptional regulators and cytokines highlighting a difference in IFN signalling following LPS and Pam3CSK4 stimulation. IRF and STAT genes had been identified by k-means clustering, predicted upstream analysis, and canonical pathway analysis as being differentially activated between LPS and Pam3CSK4 stimulations. To test if this difference in expression of IRF and STAT genes resulted in differential expression of IFN regulated genes we used a list of human type 1 IFN regulated genes generated from the Interferome database v2.
Comparison of Differential Transcriptional Expression Following Either in vitro Stimulation or in vivo LPS Stimulation of Human Whole Blood In vivo LPS stimulated human whole blood data was obtained from the Gene Expression Omnibus (GSE3824  [16] ), we analysed this data within Genespring (v12. 6) using the same methodological principles used to analyse our own data. It can be seen that as opposed to in vitro LPS stimulation, following human in vivo LPS administration the transcriptional response had returned to baseline by 24 hours (Figure  7A ). Comparative analysis of the significantly differentially regulated gene lists over 24 hours (pertime point analysis was not possible due to the different time points sampled, transcript level analysis not appropriate due to the difference in technology platforms), revealed that approximately 40% of the significantly differentially regulated genes following in vivo LPS administration were also differentially regulated following in vitro LPS stimulation.
Acknowledgments We thank the volunteer participants. We thank  Jonathan Stoye ,  Douglas Young ,  Leona Gabrys  ˇovaand  Krzysztof Potempa  ( Medical Research Council, National Institute for Medical Research (MRC-NIMR) ) for discussion and input. We thank  Abdul Sesay  and  Harsha Jani ,  Systems Biology core facility MRC-NIMR  for help with microarray samples. We thank  Fotini Rozakeas  ( MRC-NIMR ) for help with recruitment of volunteers and phlebotomy.
The ends of most contigs were incomplete IS elements of different types. Because the contigs ending in the same IS type usually did not overlap, we expected a slight underestimation of the percentage of repetitive elements of Cardinium cBtQ1 genome. See supplementary materials and methods, Supplementary Material online (supplementary file S1, Supplementary Material online) for a detailed description of the assembling and annotation procedure, as well as the software used. TBLASTX was used to compare the gene content between plasmids of Cardinium cBtQ1 and cEper1, and BLAST hits and gene order were plotted with the genoPlotR package  (Guy et al. 2010 ) from R software (R Development Core Team 2011).
Phylogenetic and Phylogenomic Analyses High-quality sequenced 16S rRNAs (including fragments of more than 900 bp) were downloaded from SILVA  (Quast et al. 2013)  and National Center for Biotechnology Information (NCBI) databases. ssu-aligner was employed for the alignment, because it takes into account the secondary structure (based on covariance models) of the 16S rRNA genes  (Nawrocki 2009). Predefined masking was selected to ensure reproducibility in future alignments, and finally, the alignment was refined with Gblocks  (Castresana 2000)  allowing 50% in coverage gaps. General time reversible, with estimates of invariant sites and gamma-distribution among-site rate variation (GTR+I+G), was selected as the best model using jModeltest2  (Darriba et al. 2012). Additionally, amino acid sequences for the gyrB gene were downloaded from the NCBI database, aligned with MAFFT using the L-INS-i algorithm  (Katoh et al.
2002)  and refined with Gblocks. ProtTest3  (Darriba et al. 2011)  gave the improved general amino-acid replacement matrix, with gamma distributed rates across sites (LG+G) as the best evolutionary model. All accession numbers are supplied in the supplementary table S1, Supplementary Material online. For phylogenomic reconstructions, 37 orthologous single copy genes related to the translation/transduction machinery and protein folding functions that were present in 61 Bacteroidetes genomes (with the exception of Candidatus Sulcia mulleri CARI that lacked 9 of these genes) and a non-Bacteroidetes species, used as outgroup, were selected using the homology search tool from the Microbial Genome Database  (Uchiyama 2003)   The first 15 BLASTP hits using the encoded proteins of Cardinium cBtQ1 cgl gene (CHV_c0068) and the Leishmania major genes cgl (cystathionine gamma-lyase, LmjF35.
IS elements were detected as described previously  (Gil et al. 2008) , refined using the web server ISsaga  (Varani et al. 2011 ) and deposited in ISfinder database. Reference copies for each mobile element were used to search with BLASTX against the nonredundant NCBI database (1e À3 e-value cutoff). The 25 best hits for each mobile element were used as the input for MEGAN4  (Huson et al. 2011) , and taxonomical assignments were done with the default LCA parameters. The genomes of A. asiaticus, Cardinium cEper1, and Cardinium cBtQ1 (with contigs concatenated in decreasing order, excluding the plasmid) were used to compute nucleotide synteny blocks with progressive Mauve aligner  (Darling et al. 2010). Cardinium cBtQ1 was set as the reference for gene order and the alignment was plotted with the genoPlotR package.
Some gene clusters were manually refined because OrthoMCL failed to recognize some orthologous genes in endosymbionts. Orthologous genes for A. asiaticus and both Cardinium strains were classified as core genome (genes shared by the three genomes), genes shared by two of the three organisms and strain-specific genes (supplementary table  S2 , Supplementary Material online). Euler diagrams were obtained with gplots package  (Warnes et al. 2013 ) from R.
Last Common Ancestor Reconstruction All genomes used in the OrthoMCL clustering method were used to reconstruct the putative last common ancestor (LCA) gene contents for each node in the phylogeny (fig. 1  and supplementary table  S3 , Supplementary Material online). The MPR (most parsimonious reconstruction) function in ape package  (Paradis et al. 2004 ) from R was used to infer the ancestral state for each character (gene clusters) in each node (LCA). Pseudogenes for all genomes were manually revised. For orthologous cluster assignments of the pseudogenes, TBLASTX was used (e value of 1e À5 and an overlap of 80% query subject) against the proteins present in the orthologous clusters. Pseudogenes that did not modify the LCA reconstruction (strain-specific genes) were not considered. Pseudogenes that were mobile elements were also excluded.
Comparing the number of gene clusters of LCA2 to LCA1, and to both Cardinium and A. asiaticus, we observed several differences among COG categories (fig. 7B , supplementary table  S3 , Supplementary Material online). First, A. asiaticus showed 331 strain-specific gene clusters, distributed in several categories, not present in LCA2. Second, the reductive evolution of the Cardinium lineage was more clearly observed in several COG categories, such as E, G, H, S, T, and V. The absence of gene clusters in Cardinium for the N category was probably due to the fact that some genes related with motility have not been yet annotated in the COG database, especially those involved in gliding motility (see later) that are, in fact, present in Cardinium cBtQ1, A. asiaticus, LCA1, LCA2, and LCA4.
Acknowledgments F. J. S. , A. L. , A. M. , D. S. -G. , E. Z. F. , F. V. , and L. M. conceived the study. F. J. S. , A. L. , A. M. , and D. S. -G. designed the experiments. D. S. -G. and F. B. performed the experiments. D. S. -G. and F. J. S. generated the data. D. S. -G. , F. J. S. , and A. L. analyzed the data. D. S. -G. , F. J. S. , and A. L. wrote the paper. F. J. S. , A. L. , A. M. , D. S. -G. , F. B. , P. -A. R. -F. , E. Z. F. , F. V. , and L. M. critically revised the paper. This work was supported by grants  BFU2012-39816-C02-01  (cofinanced by  FEDER funds and Ministerio de Economı ´a y Competitividad, Spain)  to A. L. , Prometeo /2009/092  ( Conselleria d'Educacio, Generalitat Valenciana, Spain ) to A. M. , and  EU   COST Action   FA0701. These results have been achieved within the framework of the 1st call on Mediterranean agriculture carried out by ARIMNet, with funding from  MOARD  ( IL ),  ANR (FR) ,  INIA (ES) ,  NAGREF-DEMETER (GR) , and  GDAR (TR).
Accession numbers for noncollapsed branches are displayed. Only Bayesian posterior values above 0. 5 are displayed (blue), and branches under this score were condensed. Bootstrap values from maximum likelihood phylogenetic reconstruction above 50% are also displayed (black). Bacterial Genome Cardinium cBtQ1 a Cardinium cEper1 b A. asiaticus 5a2 Host Bemisia tabaci Encarsia pergandiella Acanthamoeba spp. Chromosome Plasmid Chromosome Plasmid Chromosome Contigs 11 1 1 1 1 Size (kb) 1,013 52 887 58 1,884 GC (%) 35 32 36 31 35 CDS 709 30 841 65 1,557 Average CDS length (bp) 1,033 1,389 911 733 990 Coding density (%) 79. 7 80. 1 85. 5 82. 1 81. 8 rRNAs 3 - 3 - 3 tRNAs 35 - 37 - 35 Other RNA genes 2 - - - - Pseudogenes (total) 156 4 3 - 222 Pseudogenes (transposase) 132 3 3 - - Pseudogene (other CDS) 24 1 - - - Reference This study Penz et al.
, 2008)  defined weight of terms, stylistic  (Grabar et al. , 2007)  or discursive  (Goeuriot et al. , 2007)  features, lexicon  (Miller et al. , 2007) , morphological features (Chmielik and  Grabar, 2011)   and A3 ), and in the derived datasets unanimity and majority. nations of different features  (Wang, 2006; Zeng-Treiler et al. , 2007; Leroy et al. , 2008). Specific task has been dedicated to the lexical simplification within the SemEval challenge in 2012 1. Given a short input text and a target word in English, and given several English substitutes for the target word that fit the context, the goal was to rank these substitutes according to how "simple" they are  (Specia et al. , 2012). The participants applied rule-based and/or machine learning systems. Combinations of various features have been used: lexicon from spoken corpus and Wikipedia, Google n-grams, WordNet  (Sinha, 2012) ; word length, number of syllables, latent semantic analysis, mutual information and word frequency (Jauhar and Specia, 2012); Wikipedia frequency, word length, n-grams of characters and of words, random indexing and syntactic complexity of documents  (Johannsen et al.
This dataset contains compounds (abdominoplastie (abdominoplasty), dermabrasion (dermabrasion)), constructed (cardiaque (cardiac), acineux (acinic), lipoïde (lipoid)) and simple (acné (acne), fragment (fragment)) words. These data are annotated by three speakers 25-40 year-old, without medical training, but with linguistic background. We expect the annotators to represent the average knowledge of medical words amongst the population as a whole. The annotators are presented with a list of terms and asked to assign each word to one of the three categories: (1) I can understand the word; (2) I am not sure about the meaning of the word; (3) I cannot understand the word. The assumption is that the words, which are not understandable by the annotators, are also difficult to understand by patients.
Generation of the features We exploit 24 linguistic and extra-linguistic features related to general and specialized languages. The features are computed automatically, and can be grouped into ten classes: Syntactic categories. Syntactic categories and lemmas are computed by TreeTagger  (Schmid, 1994)  and then checked by Flemm  (Namer, 2000). The syntactic categories are assigned to words within the context of their terms. If a given word receives more than one category, the most frequent one is kept as feature. Among the main categories we find for instance nouns, adjectives, proper names, verbs and abbreviations. Presence of words in reference lexica. We exploit two reference lexica of the French language: TLFi 2  and lexique. org 3. TLFi is a dictionary of the French language covering XIX and XX centuries.
Number of bases and affixes. Each lemma is analyzed by the morphological analyzer Dérif  (Namer and Zweigenbaum, 2004) , adapted to the treatment of medical words. It performs the decomposition of lemmas into bases and affixes known in its database and it provides also semantic explanation of the analyzed lexemes. We exploit the morphological decomposition information (number of affixes and bases). Initial and final substrings of the words. We compute the initial and final substrings of different length, from three to five characters. Number and percentage of consonants, vowels and other characters. We compute the number and the percentage of consonants, vowels and other characters (i. e. , hyphen, apostrophe, comas). Classical readability scores. We apply two classical readability measures: Flesch  (Flesch, 1948)  and its variant Flesch-Kincaid  (Kincaid et al.
Machine learning system The machine learning algorithms are used to study whether they can distinguish between words understandable and non-understandable by laymen and to study the importance of various features for the task. The functioning of machine learning algorithms is based on a set of positive and negative examples of the data to be processed, which have to be described with suitable features such as those presented above. The algorithms can then detect the regularities within the training dataset to generate a model, and apply the generated model to process new unseen data. We apply various algorithms available within the WEKA  (Witten and Frank, 2005)  platform. The annotations provided by the three annotators constitute our reference data. We use on the whole five reference datasets (Table  1 ): 3 sets of separate annotations provided by the three annotators (29,641 words each); 1 unanimity set, on which all the annotators agree (n=22,925); 1 majority set, for which we can compute the majority agreement (n=28,763).
Evaluation The inter-annotator agreement is computed with the Cohen's Kappa  (Cohen, 1960) , applied to pairs of annotators, which values are then leveraged to obtain the unique average value; and Fleiss' Kappa  (Fleiss and Cohen, 1973) , suitable for processing data provided by more than two annotators. The interpretation of the scores are for instance  (Landis and Koch, 1977) : substantial agreement between 0. 61 and 0. 80, almost perfect agreement between 0. 81 and 1. 00. With machine learning, we perform a ten-fold cross-validation, which means that the evaluation test is performed ten times on different randomly generated test sets (1/10 of the whole dataset), while the remaining 9/10 of the whole dataset is used for training the algorithm and creating the model. In this way, each word is used during the test step.
The success of the applied algorithms is evaluated with three classical measures: R recall, P precision and F F-measure. In the perspective of our work, these measures allow evaluating the suitability of the methodology to the distinction between understandable and non-understandable words and the relevance of the chosen features. The baseline corresponds to the assignment of words to the biggest category, e. g. , I cannot understand, which represents 66 to 74%, according to datasets. We can also compute the gain, which is the effective improvement of performance P given the baseline BL (Rittman, 2008): P -BL 1-BL. 6 Automatic analysis of understandability of medical words:.
Annotations and inter-annotator agreement The time needed for performing the manual reference annotations depends on annotators and ranges from 3 to 6 weeks. The annotation results presented in Table  1  indicate that the annotators 1 and 2 often provide similar results on their understanding of the medical words, while for the third annotator the task appears to be more difficult as he indicates globally a higher number of nonunderstandable words. The non-understandable words are the most frequent for all annotators and cover 66 to 70% of the whole dataset. The interannotator agreement shows substantial agreement: Fleiss' Kappa 0. 735 and Cohen's Kappa 0. 736. This is a very good result, especially when working with linguistic data for which the agreement is usually difficult to obtain. The evolution of annotations per category (Figure  1 ), such as provided by the annotators, can dis- tinguish easily between the three categories: (1) the most frequently chosen category is I cannot understand and it grows rapidly with new words; (2) the next most frequently chosen category is I can understand, although it grows more slowly; (3) the third category, which gathers the words on which the annotators show some hesitation, is very small.
Given the proximity between the lines in each category, we can conclude that the annotators have similar difficulties in understanding the words from the dataset. We tested several machine learning algorithms to discover which of them are the most suitable to the task at hand. In Table  2 , with results computed on the majority dataset, we can observe that the algorithms provide with similar performance (between 0. 85 and 0. 90 P and R). In the remaining of the paper, we present results obtained with J48  (Quinlan, 1993). Table  3  shows P, R and F values for the five datasets: three annotators, majority and unanimity datasets. We can observe that, among the three annotators, it is easier to reproduce the annotations of the third annotator: we gain then 0. 040 with F comparing to the two other annotators.
A1 A2 A3 Una. Maj. P 0. 794 0. 809 0. 834 0. 946 0. 876 R 0. 825 0. 826 0. 862 0. 949 0. 889 F 0. 806 0. 814 0. 845 0. 947 0. 881 Table  3 : J48 performance obtained on five datasets (A1, A2, A3, unanimity and majority). In Table  4 , we indicate the gain obtained by J48 compared to baseline: it ranges from 0. 13 to 0. 20, which is a good improvement, despite the category I am not sure that is difficult to discriminate. We also indicate the accuracy obtained on these datasets.
A1 A2 A3 Una. Maj. BL 0. 66 0. 67 0. 70 0. 74 0. 71 F 0. 806 0. 814 0. 845 0. 947 0. 881 gain 0. 14 0. 13 0. 14 0. 20 0. 16 Acc. 0. 825 0. 826 0. 862 0. 948 0. 889 Table  4 : Gain obtained for F by J48 on five datasets (A1, A2, A3, unanimity and majority).
Impact of individual features on understandability of medical words To observe the impact of individual features, we did several iterations of experiments during which we incrementally increased the set of features: we started with one feature and then, at each iteration, we added one new feature, up to the 24 features available. We tried several random orders. The test presented here is done again on the majority dataset. Figures 2 present the results obtained in terms of P, R and F. Globally, we can observe that some features show positive impact while others show negative or null impact: • with the syntactic categories (POS-tags) alone we obtain P and R between 0. 65 and 0. 7. The performance is then close to the baseline performance. Often, proper names and abbreviations are associated with the non-understandable words.