dataesr/scientific-paragraphs-categorization huggingface dataset:
Since 1998, the University of Central England in Birmingham has been developing WebCorp1, a system for extracting linguistic data from the web, presenting examples of word usage from the Web in a form suitable for linguistic analysis. Similarly, KWICFinder2 and WebAsCorpus. org3, launched in 2007 by William Fletcher, can produce concordances from webpages. Guo and Zhang (2007)  have built a customized collocations collector that can be used by language users, and  Wu et al. (2009) , acknowledging the heterogeneous, uncontrolled, and messy nature of web data, have explored the use of web searches as a language learning tool and used the Greenstone digital library software4 to organize raw online data that can be sifted through by language learners. But if Google enthusiasts insist on using raw online data, one way of dealing with the messiness and potential unreliability of the search results can be to use Google Custom Search5, a service launched by Google in 2006 which allows creators to select what websites will be used to search for information, thus eliminating any unwanted websites.
Google use and its impact on language development Several studies have documented the impact of the web and search engines on language development and writing improvement  (Acar, Geluso, & Shiki 2011; Clerehan, Kett, & Gedge 2003; Conroy 2010; Johnson 2004; Kennedy & Miceli 2010; Kenworthy 2004; Krajka 2000 , Mansor 2007). Shei (2008a Shei ( , 2008b) )  has shown that Google searches make it possible to compare the frequency of extended collocations (combinations of up to four words) and find the most commonly used and hence more formulaic ones. This suggests that Google output, however messy it is, can be used by second-language learners to explore native-speaker discourse and increase their language awareness. Various studies have shown that some learners are keen users of information-related web services (e.
g. Schroeder et al. 2010; Palfrey & Gasser 2008). Conroy (2010)  reports that his students enthusiastically used Google and traditional concordancers for language learning and error correction but that training was a key factor in getting them to use the approaches successfully. Although Google is a useful writing support tool, deciding which errors are amenable to correction needs further exploring. He also explains that students, being regular Google users, are more likely to favour the search engine than traditional corpora for which new interfaces have to be learnt, something learners sometimes find off-putting. Sun (2003)  and  Hafner and Candlin (2007)  also found that learners preferred using Google to concordancers to learn about idiomaticity. As  Shei (2008b)  puts it, Google "remains a constant companion to the learner in the absence of the tutor.
Explaining that there is no handbook or set of rules to consult in order to avoid or fix those types of errors, she opted, in part, for direct correction hoping it "would, if nothing else, provide input for acquisition of these idiomatic forms" (p. 6). Noting that 50% of all errors she identified in her students' compositions were "untreatable," she argued that "ESL writing teachers would do well to give much more thought to how they provide error feedback regarding these different types of language forms and structures" (p. 6). This study attempts to build on existing research into error treatment and especially the role Google can play in stimulating language awareness and enhancing self-editing skills. "Untreatable" errors arguably occur when students are trying to emulate native speakers, working with their interlanguage, building on it using their acquired knowledge of rules and repository of words and expressions to formulate increasingly complex occurrences.
The rationale behind this was that learners could scour the results and borrow the native-like linguistic material their interlanguage precluded them from formulating themselves, and then weave it into their own formulations. For example, if learners want to write about the need for politicians to implement an assault weapons ban, they were shown that by entering ban followed by assault weapons in the search box, Google generates a series of results which can then be observed and borrowed from (see Figure  1 ). Using these examples, it is possible to write a series of forceful arguments like "politicians need to introduce new legislation to ban assault weapons" (using the first snippet), "US politicians must make efforts to reinstate an assault weapons ban as part of a comprehensive plan to address gun violence" (using the second snippet), and "politicians must vote on measures banning the sale of assault weapons and highcapacity ammunition" (using the third snippet).
Feedback on Google-driven language learning Sixteen completed questionnaires were returned via email (the responses to the seven 5-point Likert-scale questions are given in Table  4 ). Questions 1 to 4 show that a majority of students felt comfortable with the use of basic Google search options. Question 5 indicates that the students view Google use as a good way to correct their errors and improve their English, and question 6 indicates that a majority view it as a good way to find native-like formulations in the search results. However, only nine students said that they intended to use it in the future for linguistic purposes. In the answers they provided to the open-ended questions the students explained in more detail what they liked about Google search but also raised a number of issues.
Discussion The purpose of this study was to document the way in which internet searches can act as "a tool helping second language writers make decisions about their writing"  (Acar et al. 2010: 6). It can now be argued that using Google Custom Search and restricting searches to information websites is a way to increase the reliability of raw online data in so far as it maximizes the students' chances to be exposed to grammatically accurate English. For teachers who generally choose to reformulate "untreatable" passages in their students' papers, this can surely "help relieve [them] of the need to act as proofreading slaves"  (Milton 2006: 125 ). One student for example said he found that Google was a good way to go about correcting his errors when the teacher was not around. So it seems that Google acts as a gateway to a repository of formulations that they can choose by themselves instead of relying on their teacher to provide alternatives.
, 2003; Sun, 2003; Yoon & Hirvela, 2004; Lavid, 2007; Johns et al. , 2008; Boulton, 2009b; Liu & Jiang, 2009; ). The challenge for teachers is thus to provide learners with appropriate training and make sure they are "adequately equipped"  (Kennedy & Miceli, 2001: 81)  before exploring corpora on their own. When working on Google output, teachers are also faced with the difficult task of encouraging learners to assimilate the formulations they identify because they will inevitably risk being stigmatized for working too closely with their sources and accused of plagiarism. Donahue (2008)  points to this major problem that language teachers are grappling with and makes the case that copying should nonetheless not be castigated as plagiarism: How   2008; Bloom 2008; Bloch 2008; Adler-Kassner et al.
2008 ) explains that accusations of plagiarism are most often sweeping generalizations of otherwise skillful use of appropriated material. It may not be really fair to accuse students who borrow and use without referencing of intellectual theft as, when copying, they are learning to situate their discourse in relation to others'. Within the framework of this experiment, it has been shown that selective reading of Google results is a way for EFL students to write better English by skillfully copying and integrating prefabricated ideas and language into their own essays. The students never transfer extensive verbatim passages to their essays but select relevant multi-word fragments and the result is language hybridity (i. e. a combination of material identified in Google snippets and personal utterances).
Conclusion The web should not be dismissed as an unreliable source of data. Although it is arguably not a corpus, EFL learners can nonetheless profitably use Google for quick and easy access to authentic language in the form of selected passages from a great number of articles. In that sense, Google output is very much adapted to students who need to keep up with world events and whose ultimate goal is to emulate the language of the press. Depending on their competence, it is a vast repository of formulations that they can identify and borrow for further use in their own writing. Students can be given a significant linguistic boost if encouraged to plunder formulations featured in Google results. Such an approach implies for the students to go through an initial stage of teacher-controlled imitation (or micro-plagiarism) because initially copying native speakers will, arguably, make it possible to emulate them.
The rationale behind customizing a search engine to explore linguistic material from a selection of online newspapers is in keeping with Tribble's recommendation that the most useful corpus for EFL learners is "the one which offers a collection of expert performances in genres which have relevance to the needs and interests of the learners. Collections of relevant expert performances will exemplify the results of the desired forms of language behavior that learners are trying to achieve" (1997: n. pag. ). The main objection raised by a certain number of students who took part in this study was that they sometimes felt overwhelmed with search results or could not think of ways to formulate their queries. Further research could thus profitably focus on how best to train EFL learners to use Google search results in order to self-edit.
91 CaCl 2 , 10 EGTA (free calcium estimated at 50 nM), 10 HEPES (osmolarity set at 307 mOsm/L and pH 7. 4 with CsOH). Voltage-clamp and data acquisition were performed using an Axopatch 200B (Axon Instruments, Union City, CA) amplifier coupled to pClamp control software (v10; Axon Instruments) via an analog to digital converter (Digidata 1440; Axon Instruments). Signals were prefiltered at a 5-kHz bandwidth and sampled at 1 kHz. All experiments were performed on cells with seal resistances of 1 to 10 GΩ and at a holding potential of 0 mV. TRPA1 opener and compound solutions in external buffer were applied via multibarreled, pressure ejection pipettes controlled by electromagnetic switch valves (Warner Instruments, Holliston, MA).
Data Handling and Analysis Data analyses and graphical presentation were performed using a combination of IonWorks (v2. 0; Molecular Devices), IDBS XLfit (v4. 2; IDBS, Guildford, UK), Microcal Origin (v8. 6; OriginLab Corporation, Northampton, MA), and pClamp (v10; Axon Instruments) software. All data are given as mean ± standard deviation. For the IonWorks data, current amplitudes were measured from the ramp protocol at +50 mV from each read and the drug-evoked signal calculated by subtracting the precompound from the postcompound amplitude. Concentration-response curves were fitted using a four-parameter logistic equation of the form ( ) ( ) 0 1 2 / (1 / ) 2, p y A A x x A   = - + +   where A1 is the maximum asymptote, A2 is the minimum asymptote, x 0 is the XC 50 , and p is the Hill slope.
Pharmacological Validation Using Conventional Patch-Clamp Technique and Transfer onto the Automated Patch-Clamp System IonWorks Quattro for an hTRPA1 Agonist HT Assay A human TRPA1 stable cell line was generated using an inducible expression system allowing transient expression of the gene of interest while decreasing toxicity due to channel expression. One day after doxycycline addition to the culture media, TRPA1 protein induction occurred and recordings were performed using the manual whole-cell patch-clamp technique. In voltage-clamp studies, perfusion of 5 µM AITC induced reversible strong currents (Fig. 1A ) in human TRPA1-expressing cells, which was not observed in noninduced cells (data not shown). Coapplying the TRPA1 blocker A-967079 at increasing concentration (from 30 nM to 3 µM) induced a dosedependent inward and outward current block.
2A ). 11, 12 Note that the kinetics of the responses was modulated and a much slower response was obtained when using H 2 O 2. This correlates with published data that describe an indirect mode of TRPA1 activation by this agonist. 11 ean Z′ value determined with the high (only calculated with AITC) and low (vehicle only-0. 3% DSMO) control well currents was greater than 0. 7. Assay stability and robustness were assessed over 10 weeks. Application of the 10-µM AITC-triggered current amplitude was consistent, and variations were small from assay to assay (data not shown). With the added consistency and PPC precision, it proved possible to construct robust agonist concentration-response curves. As shown in Figure  2B , agonist elicited concentration-dependent currents that could be fitted with a fourparameter logistic equation.
No current was detected when A-967079 was added with buffer (data not shown). Four published antagonists-A-967079, ruthenium red, AP-18, and HC-030031-were then screened on the same plate (n = 8 for each tested compound). The percentage of inhibition following application of 10 µM AITC was calculated and data were fitted, giving an IC 50 values of 20 ± 2 nM, 257 ± 22 nM, 397 ± 33 nM, and 3. 84 ± 1. 04 µM, respectively (Fig. 3B ). These pharmacological data matched with previously published IC 50 values even if different technologies (such as calcium mobilization fluorescence assays) were used. 8, 14, 15 ollowing these positive results obtained with the human TRPA1 antagonist assay, we investigated a similar approach with the rodent ortholog, mTRPA1. Once again, showed that several TRPA1 agonists (AITC, menthol, and H 2 O 2 ) elicited mTRPA1 currents, and these currents could be blocked when 1 µM A-967079 was preincubated (Fig.
3C ). Finally, four known antagonists (already described in the human TRPA1 assay)-A-967079, ruthenium red, AP-18, and HC-030031-were tested in parallel on the same assay plate on several runs using AITC as the opener. Due to the high reproducibility of current amplitudes from well to well, we were able to extract IC 50 values for each tested compound (n = 8). The percentage of inhibition following application of 30 µM AITC was calculated and data were fitted, giving IC 50 values of 149 ± 12 nM, 950 ± 121 nM, 1. 11 ± 0. 12 µM, and 4. 95 ± 0. 66 µM for A-967079, ruthenium red, AP-18, and HC-030031, respectively (Fig. 3D ).
9 Note that assay success was greater when channel induction was performed only 6 h before assaying (probably due to TRPV1 expression toxicity). Also, a lower doxycycline concentration (10 ng for TRPA1 vs. 1 ng for TRPV1) was used to improve assay quality with Z′ > 0. 6. Noninduced cells did not elicit any current following capsaicin application (data not shown). With a high seal success and reliable average resistance (36. 2 ± 4. 2 MΩ, n = 384), pharmacological experiments were possible. The concentration-response curve for capsaicin was performed and showed an EC 50 value of 33 ± 3 nM (n = 16), similar to published data. 16 In addition, an IC 50 value for A784168 was determined using 150 nM capsaicin as the opener (data not shown). When the curve-fitting process was applied, an estimated IC 50 value of 7.
Introduction Genome sequencing capabilities have expanded exponentially in the last 10 years and the number of finished or draft genomes has considerably enriched databases  [1]. This growing bulk of data has already proven to be useful for strain-to-strain comparisons, to obtain novel information on pathogen diversity, as well as to rapidly identify bacterial genes that play a role in infection and SNPs or other mutational events that shape the virulence in clonal expansion of strains. Several sequencing techniques are now available at low cost that can address all these issues. However, access to bioinformatic tools that rapidly provide a user-friendly way to compare neo-sequenced genomes with references available in databases is required for microbiologists. Pseudomonas aeruginosa, a Gram-negative environmental species and an opportunistic pathogen, is a major cause of infections in vulnerable patients with cystic fibrosis (CF) or admitted to intensive care units (ICUs).
Draft genome versions were obtained by using Illumina Hiseq 2000 system, Genostar suite software and WallGene facilities allowing to track possible genetic events that could sustain their phenotypes. The clinical strains were compared to reference strain PAO1. Applied to CF (chronic evolution) and ICU (fast evolution) P. aeruginosa strains, our data provide new insights into the genomic diversification of clones, the potential role of prophages in shaping bacterial genomes in specific environments and key interesting genetic or genomic events that point out mechanisms that need further investigation.
Genome Examination We obtained the assembled draft genomes of these five strains and compared them with reference strain PAO1, a primary analysis that will be completed by using other published annotated genomes as comparators  [24]. We investigated the overall genomic differences between PAO1 and KK strains by aligning the genomes using MAUVE 2. 3. 1 software (Figure  3A ). Alignment of the assembled contig sequences of draft genomes of KK strains with PAO1 suggests a high level of conservation along the chromosome. The KK strains have their chromosomes organized in a very similar way than in PAO1 except one inversion of two physically close syntenic blocks colored in green and yellow in Figure  3A  (highlighted by arrows). Based on the total length of KK assembled contig sequences, as well as on the size of the PAO1 genome (6,264,404 bp), we could estimate the percentages of genome covered to be 97% for KK1 and KK14, and 96.
6% for KK72. KK1 has an estimated genome size of 6,759,575 bp (6219 ORF with a GC% of 63. 7), KK14 of 6,690,898 bp (6,157 ORF with a GC% of 64. 1) and KK72 of 6,657,327 bp (6132 ORF with a GC% of 63. 9) (Table  1 ), suggesting a slight reduction in the genome size during the pulmonary colonization timescale. The draft genomes of the three KK strains were compared with that of PAO1 using the WallGene software. KK1, KK14 and KK72 were thus found to share 5308 genes with PAO1 and to contain 911,849,824 specific genes in their accessory genome, respectively. The KK strains shared a core of 6019 genes and had 84, 22 and 57 genes of difference between them, respectively (Figure  3C ), while 222, 225 and 251 genes of PAO1 were absent in KK1, KK14 and KK72, respectively. We investigated the overall genomic differences between PAO1 and ST395 strains by aligning the three genomes using the MAUVE 2.
3. 1 software (Figure  3B ). Based on the total length of ST395 assembled contigs, as well as on the size of the PAO1 genome, we could estimate the percentages of genome covered to be 97. 5% and 97. 7% for ST395E and ST395P genomes, respectively. ST395E has a genome size of 6,993,173 bp (6,507 ORF with a GC% of 65. 9) while ST395P has a genome of 7,133,660 bp (6,604 ORF with a GC% of 63. 1) (Table  2 ). The draft genomes of these two ICU strains were compared with that of PAO1 using WallGene. From these results, it appeared that ST395E and ST395P shared 5322 genes with PAO1 but contained 1185 and 1282 additional accessory genes, respectively. ST395 strains had 6135 genes in common but differed by 192 and 289 specific genes, respectively (Figure  3C ). On the other hand, 189 and 181 genes present in PAO1 were lacking in ST395E and ST395P genomes, respectively.
The region KK_7 (prophage-like KK_4 corresponding to 38 ORF with a GC% of 61. 1) occurring in all the KK strains appeared to be incomplete since present at the ends of contigs in all these bacteria. PHAST software was unable to ascertain the phage origin of this region, probably because of incomplete sequences of the draft genomes. Region KK_8 (31. 5 kb, 20 ORF) was present in all the KK strains while absent from PAO1. This region carries the pltLABCDEFGMR locus involved in biosynthesis of antifungal product pyoluteorin, a hybrid polyketide-nonribosomal peptide molecule of Pseudomonas fluorescens Pf-5  [26]  also produced by LESB58 and M18 strains of P. aeruginosa. Whether it contributes to antifungal defence in the CF lungs has to be elucidated. Region KK_9 (125 kb) was found to be inserted in all the KK strains between gene PA2818 encoding the aminoglycoside response regulator Arr  [27]  and the PA2820 gene in place of the tRNA-Gly, Gly and Glu PA2819.
Genome Sequencing and Bioinformatics Genomic DNA was sequenced on Illumina Hiseq 2000 system (Beijing Genomics Institute, BGI, China). Mean genome coverage varied between 121 to 140 X. The paired-end reads were assembled de novo using SOAPdenovo 1. 05. The resulting number of contigs varied between 55 to 125 with an average length from 70,000 to 127,000 bases. Genome sizes and sequencing data are indicated for each strain in Tables  1  and 2. Annotation of the assembled contig sequences was performed with the Genostar Suite software. The Genostar Suite, together with the reference MicroB database, is an integrated bioinformatics interactive software application dedicated to microbial genome analysis and comparison. MicroB integrates and updates data from several databases, i. e. , NCBI Reference Sequence, UniProtKB, ENZYME, Gene Ontology and KEGG.
GenoAnnot module was used to annotate the 5 genomes. A variant of the PRIAM algorithm  [74]  was used to predict the enzymatic activities of the proteins. The five annotated genomes were integrated in MicroB database. The genomic comparative analyses were performed using Genostar Suite and WallGene and datasets from the present study are available in a free access  [24]. WallGene is an interactive web application developed by Genostar (Montbonnot, France) in collaboration with the Pasteur Institute (Paris, France)  [75] , to facilitate large-scale comparative analysis by computing homologies and visualizing syntenies between a set of genomes, The WallGene visualization tools include several types of views, each with a different purpose from a biological perspective. Six views are currently available:  (1)  the Wall designed to explore homologies and syntenies in a linear way by focusing on genes of interest; (2) the Line Plot and Dot Plot tools allowing comparison 2-by-2 of the general organization of assembled genomes, and explore some biological events such as conservation, inversion, and duplication; (3) the Core Genome exploring common and specific genes of selected genomes; (4) the Circular View showing the overall organization and homologies of organisms in a set of circular graphs; (5) the Gene List view to retrieve genes and homologies according to gene name or function in tabular form.
A user guide is available on the web page  [24]. Datasets can be calculated with as reference genome any assembled genome of P. aeruginosa, or with a reference genome of choice that could be a neo-sequenced and annotated genome to compare directly with clonal derivatives. In the present example, we did both (PAO1 versus KK strains and KK1 versus KK14 and KK72, and PAO1 versus ST395 strains and ST395P versus ST395E) using the same parameters (see comparison between Figure  5  and Figure  S1  on one hand and Figure  7  and Figure  S2  on the other hand). Unidirectional blastP best hits were computed using the following parameters: 80% coverage, 40% identity and 10 -5 e-value. PHAST  [76]  was used to identify, annotate and graphically display prophage sequences within bacterial genomes. Additionally, any absent phage region from a given genome was further checked to be absent using further mapping of crude reads of this genome against the assembled genomes of strains containing it.