dataesr/scientific-paragraphs-categorization huggingface dataset:
(2016)  show that some facets of nominal syntax are symptomatic of higher-grade parameters of syntactic variation. The description of Nissart is based on a corpus of 20th century texts (as previously said, most dialects in the French territory are almost extinct) and novel data obtained from semi-speakers. Data are stored in the Nice-based database Thesoc 5. For cross-linguistic comparison with Occitan and Italo-Romance systems, the study relies on data from linguistic atlases such as ALF 6 , AIS 7 , and ASIt 8  and the material published in  Manzini and Savoia (2005). The article is organised as follows: Section 2 overviews the morphosyntactic traits that characterise the vernaculars spoken in the amphizone (e. g. , the syntax of articles and possessives, also in combination with kinship terms); Sections 3 and 4 discuss how the data overviewed in Section 2 may contribute to two possible streams of research.
The aim of the investigation was to predict the serotypes of M. haemolytica based on whole genomic sequences with the capsular gene region as target. A total of 22 strains selected to have been serotyped and to represent all serotypes were investigated by whole genomic sequencing. The BIGSdb (Bacterial Isolate Genome Sequence Database) was downloaded and installed on a Linux server. Here the sequence database was setup with unique loci at serotype level. The server allows serotypes of M. haemolytica to be predicted from whole genomic sequences and the service is available to the public for free from https://ivsmlst. sund. root. ku. dk.
haemolytica based on the variable central part of the capsular gene region as target (Fig. 1 ). The serotype prediction was based on whole genomic sequences whose analysis have become affordable for routine identification and typing. The online genomic comparisons and prediction of serotypes are done by BIGSdb (Bacterial Isolate Genome Sequence Database) which is a scalable, open source, web-accessible database system that enables comparisons of phenotypic and sequence data  (Jolley and Maiden, 2010). BIGSdb is setup with a sequence database and an isolate database. BIGSdb is written in the computer program Perl for installation on UNIX/Linux operating systems and it utilizes the PostgreSQL database and Apache web server software. Sequence homology matching uses BLAST (basic local alignment search tool)  (Altschul et al.
For comparison of target gene sequence to whole genomic sequences, the option "Align two or more sequences" was used with default settings. MLST types were extracted from the genomes by comparison to the multilocus sequence type (MLST) database for M. haemolytica (http://pubmlst. org/mhaemolytica/) (Jolley et al. , 2004; Petersen et al. , 2009). Multiple alignments and neighbour joining phylogenetic analysis including calculation of bootstrap support were done by ClustalX2  (Larkin et al. , 1997) , and MEGA7  (Kumar et al. , 2016)  was used for graphical representation of trees.
Setup of BIGSdb BIGSdb v. 1. 18 was downloaded (https://github. com/kjoll ey/BIGSdb) and installed on Linux server OpenSuse desktop 13. 1 × 64bit OS at University of Copenhagen. Sequence handling routines are provided by the BIO PERL library and EMBOSS suite of programs and BLAST v. 1. 4. 4. Further details are provided online in "About BIGSdb" at ivsmlst. sund. ku. dk. The sequence database was setup with genes being unique for each serotype as loci. Scan for alleles in the isolates database of BIGSdb was based on at least 40 % coverage and 70 % identity with blastn word size of 20 and other parameters as default settings. The server is available to the public and free to use from htt ps://ivsmlst. sund. root. ku. dk.
Analysis of the capsular gene region with respect to serotype prediction The capsular gene region published by  Lo et al. (2001)  (GenBank accession number AF170495), for a strain labelled A1, was used as a reference. In this strain, 12 open reading frames (ORF) were identified of which the first four were annotated as cpxABCD (RAST annotation as kpsT, kpsM, kpsE and wcbC) and the last four as wecBC and phyAB (RAST annotated as kpsC and kpsS). Within the central part, ORF1 and ORF2 were annotated with unknown function and ORF3 and ORF4 as UDP-N-acetyl-D-mannosamine and UDP-N-acetylglucosamine 2 epimerase encoding, respectively. Comparison between the genomic sequences of all serotypes showed that cpxABCD and phyAB genes in the capsular encoding regions were homologous in all reference strains.
Acknowledgements Tim Frederik Olsen ,  Department of Public Health and Kamaran Rahim, SUND-IT University of Copenhagen  are thanked for help with the setup and maintainance of BIGSdb. Dr. Keith Jolley ,  Department of Zoology, University of Oxford  is acknowledged for advises with the BIGSdb. Cliff Ramage ,  Moredun Scientific  is acknowledged for gaining access to the reference strain of serotype 17. The project was funded by The  Danish Veterinary Consortium and Anicon.
More recently, Facebook's Blender  [90]  and Google's Meena  [2]  have received substantial interest due to their near-human open domain conversational capabilities. A large number of general-purpose platforms and frameworks are available for chatbot delivery, such as Google's DialogFlow, 1 Microsoft Bot Framework, 2 Pandorabots, 3  and the open-source frameworks Rasa 4 and Mycroft. 5 The platforms range from socalled low-code alternatives  [26] , where implementation and maintenance may be conducted with limited or no software engineering skills, to frameworks serving as basis for larger software development projects. Platforms and frameworks for chatbot delivery typically provide integrations with a range of communication channels, including social media and chat, as well as websites and collaborative work support systems.
State of the art The advances in chatbot frameworks and platforms are key enablers of the current interest in chatbot applications. As noted in the background Sect. 6 myriad platforms and frameworks are available to support design and development of chatbots. Key advances include the application of supervised machine learning for classification and information retrieval-enabling, for example, intent prediction and identification of user sentiment  [20] , which are critical to support task-oriented conversations. Furthermore, the use of generative approaches has seen substantial progress, where end-to-end dialogue systems are applied to predict suitable responses to user input based on models built from large conversational datasets  [2, 109]. Finally, the introduction of the Transformer  [107]  as a dominant and highly effective architecture for natural language processing along with high-quality open-source libraries  [113]  have lowered the barrier to entry and make it possible to build conversational models that exhibit high generalization and coherence  [90].
Abstract. We present a statistical survey of largeamplitude, asymmetric plasma and magnetic field enhancements detected outside the diamagnetic cavity at comet 67P/Churyumov-Gerasimenko from December 2014 to June 2016. Based on the concurrent observations of plasma and magnetic field enhancements, we interpret them to be magnetosonic waves. The aim is to provide a general overview of these waves' properties over the mission duration. As the first mission of its kind, the ESA Rosetta mission was able to study the plasma properties of the inner coma for a prolonged time and during different stages of activity. This enables us to study the temporal evolution of these waves and their characteristics. In total, we identified ∼ 70 000 steepened waves in the magnetic field data by means of machine learning.
Instrumentation and steepened wave selection To probe the ambient plasma, the Rosetta spacecraft was equipped with a set of five instruments, the Rosetta Plasma Consortium (RPC;  Carr et al. , 2007) , designed to monitor particle properties and electromagnetic fields at 67P/CG. The primary focus of this study is the analysis of asymmetric magnetic field enhancements, which were observed by RPC-MAG. The latter consists of two tri-axial fluxgate magnetometer mounted 15 cm apart from each other on a 1. 5 m long boom  (Glassmeier et al. , 2007b). For the following analysis, only the outboard magnetometer data were used. However, the difference between the measured signal on the outboard and inboard magnetometer was used for quality assessment of the magnetic field measurements. If the difference exceeded 5 nT, the time interval was excluded from the analysis.
The magnetometer can either be operated in burst mode (20 Hz) or in normal mode (1 Hz). Because of the transient nature of these magnetic field signatures, we have exclusively used data sampled with a frequency of 20 Hz. Intervals for which only 1 Hz data were available were excluded from the analysis. Additionally to the magnetic field, we use electron density data from the RPC Mutual Impedance Probe (RPC-MIP;  Trotignon et al. , 2007)  and neutral gas densities obtained from the Rosetta Orbiter Spectrometer for Ion and Neutral Analysis (ROSINA;  Balsiger et al. , 2007)  comet pressure sensor (ROSINA-COPS) at suitable times. For our analysis, we have processed measurements made between 1 December 2014 and 31 June 2016. In the time periods before and after our interval of interest, cometary activity was low, resulting in mostly undisturbed solar wind being observed at Rosetta.
6  and at 21P/Giacobini-Zinner  (Tsurutani et al. , 1987)  arise when dispersive effects outweigh dissipative ones and balance the nonlinear steepening. Nonlinear hyperbolic systems are known to be able to develop shock solutions, which are difficult to treat numerically, especially for methods based on discretizing derivatives directly. Unsuitable numerical schemes may develop nonphysical solutions, e. g. , in the form of spurious oscillations. Hence, Clawpack, a software suite specifically developed to solve nonlinear conservation laws, balance laws and other first-order hyperbolic partial differential equations, was used  (Clawpack Development Team, 2019). To solve systems of nonlinear hyperbolic equations, Clawpack uses a high-resolution wave propagation algorithm  (Ketcheson et al. , 2013; LeVeque, 2002).
esa. int/psa/, ESA, 2020) and the PDS archive of NASA (https: //pds. nasa. gov/, NASA, 2020) and are publicly available. A list of Rosetta science events is made publicly available by ESA (https: //www. cosmos. esa. int/web/rosetta/science Rosetta Team, 2020, last access: 5 July 2020). A list of heliosphere current sheet crossings is provided by the Wilcox Solar Observatory and Leif Svalgaard (http://wso. stanford. edu/SB/SB. html, Svalgaard, 2020, last access: 5 July 2020). Clawpack (http://www. clawpack. org, Clawpack Development Team, 2020a) is distributed under the Berkeley Software Distribution (BSD) license at https://github. com/clawpack (Clawpack Development Team, 2020b). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/angeo-39-721-2021-supplement.
Author contributions. KO wrote the manuscript and conducted the data analysis. HR participated in the adaptation and running of the simulations. PhH validated the data analysis. KHG, BT, PhH, CG, PiH, MR and IR contributed to the interpretation of the results. KHG additionally supervised the study. SAP provided valuable insight about the nature of the structures during the review process. All authors contributed to the proofreading of the manuscript. Competing interests. The authors declare that they have no conflict of interest. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements. Rosetta  is an  ESA  mission with contributions from its member states and  NASA. We are indebted to the whole  Rosetta Mission Team, SGS  and RMOC for their outstanding efforts in making this mission possible. The RPC-MAG data are made available through the  PSA archive of ESA  and the  PDS archive of NASA. We thank  Martin Volwerk  and  Lucile Turc  for their helpful comments. Financial support. The contribution of the  RPC-MAG  and ROMAP team was financially supported by the  German Ministerium für Wirtschaft und Energie  and the  Deutsches Zentrum für Luft-und Raumfahrt  under contract  50QP1401. Research reported in this publication was supported by the  King Abdullah University of Science and Technology (KAUST). Portions of this research were performed at the  Jet Propulsion Laboratory, California Institute of Technology , under contract with  NASA.
List Of Abbreviations HDGC Hereditary Diffuse Gastric AntroPyloric Figures Figure 1 Percentage of distribution of 27 CDH1 identi ed variants: Figure 2 Family History of index cases carrying selected variants. Figure 3 Indel effect for the index case "JI-014"as predicted by Visual Interactive Biosoftware Figure 4 Detection of CDH1 exon deletions by MLPA assay. Figure 5 In silico analysis of the mutation c. 2281G>A on E-Cadherin. Figure 6 Distribution of 15 identi ed CTNNA1 variants.
4C , E, solid gray line). Based on the Guttman-Kaiser criterion, we only retained the first two principal components  [28]  (Additional file 1: Figure  S1D ). Figure  4F  shows the entire dataset projected in this unique 2D PCA space, where the temperature is color-coded. As the temperature is increased, the accessible locomotor space is shifted towards higher values of both marginal projections, with a concurrent widening of the distribution for the first PC. These observations are thus in line with the view that the trajectories are confined to a manifold defined by the correlation between the various parameters. Each temperature delimits a specific accessible region of this subspace as defined by the PCs projection values.
It has been shown that as few as 13 different swim bout types are sufficient to capture the entirety of its behavioral repertoire  [21]. Here we focus on spontaneous exploration in the absence of time-varying sensory cues. For this particular behavior, Marques et al. showed that only 4 different bout types effectively contribute to the navigation. In the present work, we further restricted the discretization to only two categories (forward and turn bouts), and extracted 5 kinematic variables, ignoring fine differences in tail bout execution. We showed that the knowledge of these 5 variables statistics nevertheless suffices to fully characterize the long-term exploratory process. Indeed, synthetic trajectories generated by stochastic sampling from the statistical distributions extracted from the data accurately reproduce the experimentally observed angular and translational dynamics.
Tracking and basic analysis Larvae were tracked offline using the open-source Fast-Track software  [22]. It generates a text file containing the position of each fish's center of mass and body angle across frames until they leave the defined ROI. Kinematic analyses were performed using MATLAB (R2020a, Mathworks). Bouts are detected when the instantaneous speed is greater than two times the overall variance of the speed. Putative bouts are then filtered on a distance criterion (bouts with a linear displacement -measured in a time window of ± 0. 5 s centered on the bout onset -less than 0. 3 mm or greater than 18 mm are rejected) and on a temporal criterion (bouts occurring within 0. 4 s after a bout are rejected). Bout timing is defined as 80 ms before the velocity peak. Detection performance was checked manually on randomly selected sequences.
From positions, time, and body angles before and after a bout event, we computed displacements, interbout intervals, and turn angles associated with each bout. Data are split into trajectories, from one edge of the ROI (set at 5 mm from the walls) to another. Only trajectories that last at least 25 s, with at least 10 bouts, with 3 bout types (left turn, right turn, and forward scoot) are kept for further analysis. Trajectories last on average 67 s (median 47 s, 95th percentile 178 s) and contain on average 60 bouts (median 44 bouts, 95th percentile 154 bouts). The number of trajectories and the number of bouts retained for further analysis are given for each temperature in Table  1. The effect of the chosen cutoffs (minimum trajectory duration and minimum interbout interval) used for trajectory and bout selection is tested in Additional file 6: Figure  S6.
Statistical methods Probability density functions (pdf ) were computed with a kernel density estimation through the built-in Matlab function ksdensity, with a bandwidth of 0. 1 for interbout intervals and displacements and 0. 5 for turn angles. For the distributions of Fig. 2 , a pdf was computed for each batch and the mean and standard error of the mean are computed. For rescaled curves (Additional file 3: Figure  S3 ), data from all experiments were pooled to compute the temperature-average quantity X T and rescaled values. Boxplots were made with the builtin Matlab function boxchart, using as input data the means of the respective quantities for trial (one dot corresponds to a batch of 10 fish). For simulations of navigation, averages over temperature were computed by pooling all bout events from all experiments corresponding to this particular temperature.
3E. Mean square displacement (MSD) d 2 and mean square reorientation (MSR) δθ 2 were computed using the MATLAB package msdanalyzer  [50]. All (x, y) and δθ sequences are pooled by temperature for both data and simulations, the MSD and MSR were computed for each sequence and we show in Fig. 6B  and C the ensemble average for each temperature with the standard error of the mean.
Algorithm After choosing a number n of fish (trajectories), we generate multivariate distributions (copulas) with the MATLAB built-in mvnrnd function, with the mean R traj T correlations matrix as input. It produces 5 marginal sets of n Gaussian random numbers, correlated with one another. We then get the corresponding normal cdf, which is in turn used to sample the corresponding Y cdfs, inversing the latter. Finally, those samples are multiplied by the corresponding temperature average X T. A bout is generated by sampling a displacement and a turning angle, along with a interbout interval during which the virtual fish stands still, from the generic cdf of. Those values are multiplied by the trajectory means drawn earlier, and the new position (x, y) is computed. The next bout is generated, and so on.
Funding This work was funded by the  CNRS, Sorbonne Université , and the  Systems Biology Network of Sorbonne Université  and supported in part by the  Human Frontier Science Program  under grant no. RGP0060/2017 , by the  French Research National Agency  under grant no. ANR-16-CE16-0017  and the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  grant agreement no. 715980. Funding bodies did not have any role in the design of the study and collection, analysis, and interpretation of data nor in writing the manuscript.
None of the clusters have an overlapping R 500 region and no signs of cluster interaction were found; however, the XMM-Newton data alone are probably not sensitive enough to detect such signs, and a joint analysis of X-ray and the thermal Sunyaev-Zeldovich effect (tSZ) is needed for further investigation, which may also reveal the presence of the warm-hot intergalactic medium (WHIM) within the system. The comparison with the other Planck -detected tripletcluster-system shows that they have rather different configurations, suggesting rather different merger scenarios, under the assumption that they are both not simply projected triplet systems.
Serendipitously, a cluster-candidate search with Planck data revealed two triplet-cluster systems with favorable system configurations for the joint analysis of a SC of this type. Both are located at redshifts where they are still sufficiently X-ray-bright and they are also sufficiently small such that we can observe the entire system within one field-of-view (FOV) of the X-ray observatory XMM-Newton, which is very well suited to detecting emission from diffuse, hot gas. The search was conducted with the data from the first Planck sky scan  (Planck Collaboration et al. 2011a ) and was followed up with an XMM-Newton observation campaign  (Planck Collaboration et al. 2011b, hereafter PLIX2011). For one of the SCs (PLCK G214. 6+36. 9), located at z ≈ 0. 45, a joint X-ray-tSZ analysis study was conducted  (Planck Collaboration et al.
Pointecouteau), hereafter referred to as the deep observation. We want to use this XMM-Newton data in conjunction with Planck and optical data to conduct a comprehensive multiwavelength analysis in order to constrain the cluster members of the system and their dynamics, and the presence and the properties of WHIM around them. This work represents the first step in this comprehensive analysis. The paper is organized as follows: Sect. 2 describes our procedure of processing XMM-Newton data, Sect. 3 explains our data analysis, which includes the surface brightness measurement (Sect. 3. 1), the spectroscopic measurement (Sect. 3. 2), the model description (Sect. 3. 3), the fitting procedure (Sect. 3. 3. 5), and the fit results (Sect. 3. 4), and in Sect. 4 we discuss them and make comparisons with the literature.
Region definitions For our analysis, we need to define several sky and detector regions shown in Figure  1. Our inFOV area is defined as a 13 circle centered on the on-axis point of the instrument. This definition avoids the most outer parts of the XMM-Newton full FOV, where the effective area is the smallest, and where the IBKG is the highest with respect to the astrophysical emission, which can make the detection of (contaminating) sources unreliable (e. g. ,  Chiappetti et al. 2013). The outFOV area is used for estimating the IBKG and we adopt the definition within the XMM-ESAS scripts 3   (Snowden et al. 2004 ) for it. The cluster region is defined as a circle of 6 radius (≈ 1. 9 Mpc) centered on the X-ray emission peak of a cluster, which is defined as the count rate(IBKG-subtracted)-weighted barycentre of the cluster.
Spectroscopy The spectrum extracted from a cluster is described with a onetemperature APEC 7  model  (Smith et al. 2001) , where its free parameters are the temperature, redshift, metallicity, and normalization. Galactic absorption is taken into account via the photoelectric absorption model phabs, for which we use a hydrogen column density of N H = 4. 59 × 10 20 cm -2 as determined by  Kalberla et al. (2005)   8  for our XMM-Newton observations and the solar metallicity of  Anders & Grevesse (1989). For the spectral fitting we use the package XSPEC 9 (v12. 10. 1f, Arnaud  1996) in conjunction with the bayesian parameter estimation package BXA 10 (v3. 3, Buchner et al. 2014), which requires the use of cstat for the fit statistic of XSPEC and makes use of the nested sampling algorithm MultiNest 11 (v3.
This would also be in line with the finding that all clusters are consistent with the scaling relation of the XXL 100 brightest clusters, where 80 % of them are not associated with a multi-cluster system (Sect. 4. 1). However, our X-ray data alone are not sensitive enough to be used to measure such an excess signal, because the XSB of each cluster drops below the CXB already within its R 500 region. Hence, a joint analysis of X-ray and tSZ data would be required to increase the sensitivity and potentially detect signs of interactions between clusters, because the tSZ effect is more sensitive to the emission in the cluster outskirts (e. g. ,  Eckert et al. 2017). The latter makes such a joint analysis particularly suitable for revealing the presence and the properties of WHIM within the system.