dataesr/scientific-paragraphs-categorization huggingface dataset:
Extended data Zenodo: Phylogenomic analyses of 142 prokaryotic genera. https://doi. org/10. 5281/zenodo. 4034244  58. Zenodo: Simulated pairs of nucleotide sequences for testing (alignment-free) genome distance estimate methods. https://doi. org/10. 5281/zenodo. 4034461  60. Zenodo: Model trees and associated simulated nucleotide sequences for testing phylogenetic inference methods. https://doi. org/10. 5281/zenodo. 4034643  61. Data are available under the terms of the Creative Commons Attribution 4. 0 International license (CC-BY 4. 0).
Two amendments in CONTINUATION-PV: 1 st Amendment: Implementation of the hydroxyurea arm to CONTINUATION-PV for collecting further data from the patients who were randomized into the hydroxyurea arm in PROUD-PV. 2 nd Amendment: Study endpoints were changed, and co-primary endpoints added (all changes to the study endpoints were finalised before the data snapshot for analysis took place). Rewording of hydroxyurea arm to best available treatment arm also included patients with other first line therapy than hydroxyurea. Efficacy parameters were collected for both arms every three months.
Randomisation and masking Randomisation was block-stratified by the following strata: previous HU treatment [yes/no], age at screening [≤60 or >60 years] and history of thromboembolic events [yes/no]). Treatment assignment was conducted using an electronic case report form system (Merge eClinical OS) at the sites. A statistician generated the randomisation list using SAS version 9. 3. PROUD-PV study was not blinded; however, the sponsor had no access to cumulative data prior to database lock.
All analyses were conducted using Statistical Analysis System software, version 9. 3 or higher (SAS Institute, Cary, NC, USA). The sample size in PROUD-PV was estimated according to the anticipated size of treatment effect in an overall rate of responders at 12 months (25% at least). The assumed rate of responders was 15% (control group) and 40% (ropeginterferon alfa-2b group). Supposed drop-out rate was 20%. The dropped-out patients were considered as non-responders according the definition of the primary endpoint which would decrease assumed rate of responders to 12% (control group) and 32% (ropeginterferon alfa-2b group). Based on these assumptions, 126 patients per group (252 in total) would be needed to be analysed in order to detect the difference in response rate between treatment groups at 1% (two-sided) significance level with 90% power using standard chi-square test.
4%; control group: 11. 8%). A further limitation of the presented studies is the lack of pathologic response data in support of hematologic and clinical efficacy outcomes, since very few patients consented to the optional bone marrow assessment. Further limitations posed by the design of the extension study are statistical power was not recalculated after the initial 12 months, and although the results indicate that the treatments groups were balanced after rollover, selection bias cannot be ruled out. 4  , and pruritus and headache. Disease-related signs and symptoms were identified in medical history records and adverse events records. The occurrence of disease related signs and symptoms were analysed descriptively by the same way as adverse events. Resolution and/or clinical improvement of disease-related signs and symptoms were binary variables (achieved, not achieved) and the same statistical methods of analysis as for the primary endpoint was used.
| Study design This was a 16-week, randomized, multicentre, open-label, parallel group, phase IV trial, conducted between October 2016 and April 2018 at 51 sites in mainland China (NCT02836704). The study design and methods have been reported previously. 23 Briefly, the study comprised a 2-week screening period, a 3-day run-in period, a 16-week randomized treatment period and 1-week follow-up period. Patients were required to cease any ongoing treatment with sulphonylureas or glinides during the run-in period, but were allowed to continue treatment with other OADs at the same dose and frequency. Use of dipeptidyl peptidase-4 inhibitors was also prohibited during the study, as these drugs were not approved as an add-on therapy to insulin in China at the time of the study. Patients with fasting plasma glucose (FPG) >16.
| Treatment and procedures All patients who met the eligibility criteria at the end of the run-in period were randomly assigned (1:1) using an interactive voice and web-response system to receive once-daily subcutaneous injection of Gla-100 (Lantus SoloSTAR ® , Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany) at a standard starting dose (0. 2 U/kg) or a higher starting dose (0. 3 U/kg). Randomization was stratified by baseline sulphonylurea or glinide use to minimize bias due to a possibly short washout period duration (the 3-day run-in period). Patients were advised to administer the insulin dose at the same time each day, preferably at dinner time. While patients, investigators and site staff remained unmasked to treatment, the statistician and sponsor remained masked until after database lock and completion of analyses.
ACKNOWLEDGMENTS This study was supported by  Sanofi. We thank the patients, principal investigators (see Supporting Information), and their staff for participating in the study. We would also like to thank  Andrea Bothwell , on behalf of inScience Communications,  Springer Healthcare ,  Julian Martins  of inScience Communications,  Springer Healthcare , and  Nishad Parkar, PhD , of  Springer Healthcare communications , who provided medical writing assistance, which was funded by  Sanofi China.
Putative FliA Promoters and Consensus Motif The consensus motif (TAAAGTTT-N11-GCCGATAA), corresponding to promoter sequences recognised by FliA  [40] , was used to search for promoter sequences recognised by FliA upstream of the flagellar genes in MFE01. The sequence logo, corresponding to putative FliA-dependant promoters in the MFE01 strain, was generated using Multiple Em for Motif Elicitation (MEME: http://meme-suite. org/tools/meme). GenBank accession numbers: flaA-to-fliT region, MT018347; flgM-region, MT018348; motA-motB-region, MT018349 2. 9. Extraction of Total RNA from P. Fluorescens MFE01 Total RNA was extracted using the hot acid-phenol protocol described by Bouffartigues et al. [41] , with modifications. Bacteria were lysed in lysis buffer (0. 02 M sodium acetate, pH 5. 5, 0. 5% (w/v) SDS, 1 mM EDTA) in early exponential growth (OD 580nm = 1).
Quantitative Reverse Transcription-PCR qRT-PCR was performed using the protocol described by Guyard-Nicodème et al. [42]. All samples were analysed independently at least six times. The primers (Table  2 ) were designed using Primer Express 3 software. A single PCR product for each primer pair was verified prior to use. The 13-µL reactions were performed using the following conditions: 6. 5 µL SYBR Green PCR Master Mix (Applied Biosystems), 0. 3 µM primers, and 3 µL cDNA. PCR reactions were performed with the 7500 Real Time PCR System apparatus (Applied Biosystems, Waltham, MA, USA). Relative quantification of the mRNAs of interest was obtained by the comparative CT (2 -∆∆CT ) method, using MFE01 recA mRNA as an endogenous control. The relative quantification (RQ) of mRNA was calculated using the 2 -∆∆CT method between the wild-type strain containing the empty vector (+EV) and the mutant strains containing the empty vector (+EV).
Peptide extraction was carried out 3 times with 100% ACN. Peptides were then dried completely using a Speedvac concentrator (SPD111V, Thermo Fisher Scientific) and stored at -20 • C. Peptides were then analysed by tandem mass spectrometry using an LTQ-Orbitrap Elite mass spectrometer coupled to an Easy nLC II system (both Thermo Scientific). Raw data files were first processed using Proteome Discoverer 1. 4 software (Thermo Scientific). Peak lists were searched using the MASCOT search software (Matrix Science) against the database Pseudomonas sp. B10 (www. pseudomonas. com). Database searches were performed with the following parameters: 2 missed trypsin cleavage sites allowed; variable modifications: carbamidomethylation on cystein, and oxidation on methionine. The parent-ion and daughter-ion tolerances were 5 ppm and 0.
35 Da, respectively. 2. 17. Flagellin and Hcp Proteins Mass Spectrometry Identification from SDS-PAGE Mass spectroscopy (MS) analyses were performed with a MALDI-TOF AutoflexIII (Brucker) in positive ion mode, as previously described  [9]. Statistical analyses of the sequences involved determining the probability based on the Mowse score with MASCOT software (peptide tolerance = 100 ppm and mass values = MH + ). A p-value of < 0. 05 was considered significant. The criteria used to accept a protein identification based on peptide mass fingerprinting (PMF) data included a probability score greater than a threshold score defined by the MASCOT software (www. matrixscience. com/search_form_select. html).
FliA Activates the Transcription of Flagellar Class IV Genes in the MFE01 Strain In Pseudomonas aeruginosa PAO1, FleQ is a transcriptional activator required for class I and II flagellar genes expression, and RpoN controls the transition from class II to class III flagellar genes expression  [31]. In 2018, Blanco-Romero et al. ascertained that the transcriptional regulator FleQ is the master regulator of the flagellar cascade in Pseudomonas because fleQ mutants of P. aeruginosa, P. putida, and P. fluorescens are non-motile and lack flagella  [29, 47, 51, 52]. Furthermore, they demonstrated by ChlP analysis that FleQ is a global regulator of motility-related genes and exopolysaccharides production in P. fluorescens F113 and P. putida KT2440. Finally, FliA acts as a checkpoint, permitting the transition of flagella from class III (hook-based-body) to class IV (filament) (see Figure  S1  for details and Table  S1  for genes function and MFE01 genes GenBank accession numbers)  [29, 47, 51, 52].
Acknowledgments: We thank members of the  Plateforme de PRotéomique et d'IMAgerie CEllulaire de Normandie  ( PRIMACEN, Université de Rouen Normandie ) and  E. Cascales  of the  IMM, Institut de Microbiologie de la Méditerranée ,  FR3479 ,  Campus CNRS ,  Marseille  for hosting our transmission electron microscopy studies. We also thank  Alex Edelman  and  Associates  for linguistic support.
Funding: This research was supported by grants from  French institutions (Rouen Normandie Université ,  Ministère de l'Enseignement Supérieur, de la Recherche et de l'innovation, Région Normandie & the Evreux Portes de Normandie agglomération ) and  FEDER (European Union ). S. M. is the recipient of post-doctoral fellowships from the  Normandy Region (SeSad network, France). This work is co-funded by the  Normandy Region  and the  European Regional Development Fund (ERDF).
Immunofluorescence analysis Immunohistochemical staining was performed on sections fixed in acetone. Following blocking with 20% FBS, sections were incubated overnight at 4°C with the primary antibodies. On the following day, the sections were washed three times with PBS and then incubated with the conjugated secondary antibodies for 1 h at room temperature. All antibodies were purchased from Abcam (U. K. ) and BioLegend and are listed in Table  1. After three washes with PBS, sections were embedded in ProLong Antifade Reagent with 4¢,6-diamidino-2phenylindole (Invitrogen). Images were acquired using an Axio Imager M2 microscope with ApoTome driven by ZEN software (Zeiss), using a 20 • /1. 0 numerical aperture objective lens. To quantify the vascular density (number of vessels per mm 2 ), four sections were analyzed for each condition.
Statistical analyses Vascular densities in three or more conditions were compared in a one-way analysis of variance with Bonferroni's test for multiple comparisons. The data are presented as median [interquartile range] (range). All statistical analyses were performed using GraphPad Prism software (version 6; GraphPad Software, Inc. , San Diego, CA). The threshold for statistical significance was set to p < 0. 05 for all tests.
Introduction As the amount of digital data continuously grows, users demand technologies that offer quick access to such data. In fact, users rely on systems that support information search interactions such as Siri 1  , Google Assistant 2  , Amazon Alexa 3  or Microsoft XiaoIce  (Zhou et al. , 2018) , etc. These technologies, called Dialogue Systems (DS), allow the user to converse with a computer system using natural language. Dialogue Systems are applied to a variety of tasks, e. g. : -Virtual Assistants aid users in everyday tasks, such as scheduling appointments. They usually operate on predefined actions which can be triggered by voice command. -Information-seeking systems provide users with information about a question (e. g. the most suitable hotel in town). These questions also include factual questions as well as more complex questions.
Characteristics As the name suggests, a task-oriented dialogue system is developed to perform a clearly defined task. These dialogue systems are usually characterized by a clearly defined and measurable goal, a structured dialogue behaviour, a closed domain to work on and a focus on efficiency. Usually, the task involves finding information within a database and returning it to the user, performing an action, or retrieving information from its users. For instance, a restaurant information dialogue system helps the user to find a restaurant which satisfies the user's constraints. Furthermore, task-oriented dialogue systems also serve as interfaces to program APIs, which is often used in the Smart Home setting  (Möller et al. , 2004). For example, an in-car entertainment dialogue system can be ordered to start playing music via voice commands or querying the agenda (see Figure  1  for an example).
g. retrieve data from a database, ask for a missing information, etc. ). Deciding which action to take is part of the dialogue control. In earlier systems, the dialogue control was based on a finite-state automaton in which the nodes represent the questions of the system and the transitions the possible user's answers. This method, while being rigid, is efficient when the domain and the task are simple. It has been widely used to design dialogue systems and many toolkits are available such as the one from the Center for Spoken Language Understanding  (Cole, 1999)  or VoiceXML. 6 he main issue is the rigid dialogue structure as well as the tendency to be error-prone. In fact, such a system does not model discourse phenomena like ellipsis (a part of the sentence structure that can be inferred from the context is omitted) or anaphoric references (which can be resolved only in a given context).
Furthermore, this architecture leads to interdependence among the modules, i. e. when one module is changed, all the subsequent modules need to be adapted as well  (Zhao and Eskenazi, 2016). Finally, the slot-filling architecture, which is often used, makes these systems inherently hard to scale to new domains since there is a need to hand-craft the representation of the state and action space  (Bordes et al. , 2017). To overcome these limitations, current research focuses on end-to-end trainable architectures where the dialogue system is trained as a single module. Wen et al. (2017)  model the dialogue as a sequence to sequence mapping, where the traditional pipeline elements are modelled as interacting neural networks. The policy network takes as input the results form the intent network, the belief tracker network, the database operator and selects the next action, based on the selected action, the generation network produces the output utterance.
In a large-scale user study, they collected 544 dialogues over 42 hours of speech. For these experiments, the authors worked with an extended number of quality measures: e. g. number of barge-ins (i. e. sudden interruption by the user), number of cancel operations, number of help requests. A survey at the end of the dialogue was used to measure the user satisfaction. The survey asked about various aspects: e. g. speech recognition performance, ease of the task, if the user would use the system again. Based on the survey, the user satisfaction score is computed and used as the target variable to train the PARADISE framework as described above. Table  6  shows the generalization scores of PARADISE for different scenarios. According to these scores, we obtain the following observations: Where ALL denotes that the collection of all the annotated data from the three different systems.
The distinction between NOVICES and EXPERTS denotes the level to which the test subjects were instructed to use the dialogue system. -A linear regression model is fitted on 90% of the data and evaluated on the remaining 10%. The results show that the model is able to explain R 2 = 50% of the variance, which is considered to be a good predictor by the authors. -Training the regression model on the data for one system and evaluating the model on the data for another dialogue system (e. g. train on the ELVIS data and evaluate on the TOOT data) show high variability as well. The evaluation on the TOOT system data yields much higher scores than evaluating on the ANNIE data. These results show that the model is able to generalize to data of other dialogue systems to a certain degree. -The evaluation of the generalizability of the model across different populations of users yields a negative result.
When trained on dialogue data from conversation by novice users (NOVICES), the linear model is not capable of predicting the scores by experienced users (ANNIE EXPERTS) of the dialogue system. The PARADISE framework is not only able to find the factors, which have the most impact on the rating, it is also capable of predicting the ratings. However, the experiments also revealed that the framework is not capable of distinguishing between different user groups. This result was confirmed by  Engelbrecht et al. (2008) , which tested the predictive power of PARADISE for individual users. User satisfaction at the exchange level. In contrast to rating the dialogue as a whole, in some cases it is important to know the rating at each point in time. This is especially useful for online dialogue breakdown detection.
Modelling Conversational Dialogue Systems Generally, there are two different strategies to exploit large amounts of data: -Utterance Selection: Here, the dialogue is modelled as an information retrieval task. A set of candidate utterances is ranked by relevance. The dialogue structure is thus defined by the utterances in a dialogue database  (Lee et al. , 2009). The idea is to retrieve the most relevant answer to a given utterance, thus learning to map multiple semantically equivalent user-utterances to an appropriate answer. -Generative Models: Here, the dialogue systems are based on deep neural networks, which are trained to generate the most likely response to a given conversation history. Usually, the dialogue structure is learned from a large corpus of dialogues. Thus, the corpus defines the dialogue behaviour of the conversational agent.
Utterance selection methods can be interpreted as an approximation to generative methods. This approach is often used for modelling the dialogue system of Virtual Humans. Usually, the dialogue database is manually curated and the dialogue system is trained to map different utterances of the same meaning to the same response utterance. Another application of utterance selection is applied to integrate different systems  (Serban et al. , 2017b; Zhou et al. , 2018). Here, the utterance selection system selects from a candidate list, which is comprised of outputs of different subsystems. Thus, given a set of dialogue systems, the utterance selection module is trained to select for the given context, the most suitable output from the various dialogue systems. This approach is especially interesting for dialogue systems, which work on a large number of domains and incorporate a large amount of skills (e.
The discriminator achieved an accuracy of 62. 5% which indicates a weak generator. However, the authors did not evaluate whether the discriminator score is a viable metric for evaluating a dialogue system. A study on the viability of adversarial evaluation was conducted by  Bruni and Fernandez (2017). For this, they compared the performance of discriminators to the performance of humans on the task of discriminating between real and artificially generated dialogue excerpts. Three different domains were used, namely: MovieTriples (46k dialogue passages)  (Serban et al. , 2016) , SubTle (3. 2M dialogue passages)  (Banchs, 2012)  and Switchboard (77k dialogue passages)  (Godfrey et al. , 1992). The GAN was trained on the concatenation of the three datasets. The evaluation was conducted on 900 dialogue passages, 300 per dataset, which were rated by humans as real or artificially generated.
The results show that the annotator agreement among humans was low, with a Fleiss  (Fleiss, 1971 ) π = 0. 3, which shows that the task is difficult. The agreement between the discriminator and the humans is on par with the agreement among the humans, except for the Switchboard corpus, where π = 0. 07. Human annotators achieve an accuracy score with respect to the ground-truth of 64% -67. 7% depending on the domain. The discriminator achieves lower accuracy scores on the Switchboard dataset but higher scores than humans on the other two datasets. In order to evaluate the ability of the discriminators on different models, a seq2seq model was trained on the OpenSubtitles dataset  (Tiedemann, 2009 ) (80M dialogue passages). The discriminator and the human performance on the dialogues generated by the seq2seq model was evaluated.
For topic classification, a Deep Averaging Network (DAN) was trained on a large amount of question data. DANs do topic classification and the detection of topic-specific keywords. The conversational data used to evaluate the topic-based metrics stems from the Alexa-Prize challenge 8  , which consists of millions of dialogues and hundreds of thousands of live user ratings (on a scale from 1 to 5). Using the DAN, the authors classified the dialogue utterances according to the topics. Conversational topic depth is measured by the average length of a sub-conversation on a specific topic, i. e. multiple consecutive turns where the utterances are classified as being the same topic. The conversational breadth is measured on a coarse-and fine-grained level. Coarse-grained topic breadth is measured as the average number of topics a bot converses about during a conversation.
, 2009). They gathered 19 dialogues and 296 utterances in a Wizard-of-Oz experiment. To allow for more diversity, they let human experts write paraphrases of the commander role to ensure that the virtual character understands a larger variety of inputs. Furthermore, the experts expanded the set of possible answers by the virtual character by annotating other candidate utterances as appropriate. The weak agreement metric was able to measure the improvement of the system when the extended dataset was applied: the simple system based on the raw Wizard-of-Oz data achieved a weak agreement of 43%; augmented with the paraphrases, the system achieved a score of 56%; and, finally, adding the manual annotation increases the score to 67%. Thus, the metric is able to measure the improvements made by the variety in the data.