dataesr/scientific-paragraphs-categorization huggingface dataset:
Implementation Our package automates the process of exporting data to the SOLAR-specific format, creating/cleaning directories (including parallel computing), passing necessary configurations to SOLAR commands, and parsing SOLAR output files to extract the results of an analysis. The user works with top-level functions which correspond to low-level SOLAR commands, as summarized in Table  1. Each function performs the analysis with default SOLAR behavior, but the user can pass a specific configuration by two arguments, for example, polygenic. settings and polygenic. options for solarPolygenic function. c Oxford University Press XXXX. Andrey Ziyatdinov 11 , Helena Brunel 1 , Angel Martinez-Perez 1 , Alfonso Buil 2 , Ramon Souto 1 , Alexandre Perera 3,4,5 and Jose Manuel Soria 1,5    The high-level functions of the package (column 3) correspond to the low-level SOLAR commands (column 2).
GNXAS is a suite of programs based on multiple scattering calculations which performs a structural refinement of EXAFS spectra. It can be used for any system although it has been mainly developed to determine the local structure of disordered substances. We developed a user-friendly graphical user interface (GUI) to facilitate use of the codes by using wxPython. The developed GUI and the codes are multiplatform running on Windows, Macintosh and Linux systems, and are free shareware (http://gnxas. unicam. it). In this work we illustrate features and potentials of this newly developed version of GNXAS (w-GNXAS).
GNXAS was originally developed for VMS, Unix, and Linux. Due to the use of obsolete Fortran expressions, the code was only able to be compiled by g77 compiler. We have recently revised the codes to suppress those old statements, thus now the code can be compiled by modern gfortran compilers. [7]  The code has been compiled in static mode, so that it is fully portable, namely the users do not need to install additional libraries and dependencies. Furthermore, GNXAS was conceived to run with command lines. When it was developed, the text based input and output files were standard, so that the users were used to the operations. Nowadays, most of computer users are familiar only with the window based operations with a mouse, therefore this old style operation creates difficulty for novice users to start using the codes.
Discussion The original GNXAS is manipulated in terms of traditional command line. For example, to run gnxas which calculates n-body distribution functions, g n , for successive processes of EXAFS analysis, one should type a command in a console window as #. /gnxas < c-Ge_xas. in where. /gnxas is a command for its execution and c-Ge_xas. in is a name of input file. The symbol "<" is for directing the input file to the executable file, which is used only for Linux and Macintosh versions of GNXAS, while Windows version does not use this symbol. For plotting output data, there are scripts provided for TopDrawer  [11]  and gnuplot  [12]. The executable of TopDrawer is provided at the site of GNXAS. Fig. 1  shows the input file c-Ge_xas. in from the data-analysis examples  [13]  as a typical example of text based input file.
All the parameters are explained in so called "red book"  [1]. We see that the text type input file is not intuitive, and one should spend some time to be familiar with it. Moreover, one should learn command line manipulations. Thus, these text based operations discourage novice users. A human friendly interface called w-GNXAS which is based on a Graphical User Interface (GUI) tool through the Python library "wxPython"  [14]  controles these text based operations of GNXAS programs. Since the Python code is platform independent, it can be executed on any modern operating systems, such as Linux, Windows and Macintosh. With this newly developed GUI, the usage of GNXAS is intended to be more intuitive and facilitated. In this release we use Python 2. 7, which is well tested and very stable. Users can control it directly typing commands on the appropriate panels and windows, on any operating systems.
(See the image of Fig. 2 ) The input parameters and the data are read via the w-GNXAS interface, and they are directly transferred to GNXAS calculations. The plotting scripts are also prepared automatically. The results may be plotted by TopDrawer (or gnuplot, or other graphical software). The left panel of Fig. 3  shows a typical image for the new w-GNXAS interface where the same input data in Fig. 1  are used. The w-GNXAS interface is constructed with several tabs in its main frame, such as crymol, gnxas, etc. , which correspond to the programs of GNXAS suite.
Acknowledgments Parts of this work have been funded by European  FP7 MSNano network  under Grant Agreement No. PIRSES-GA-2012-317554 , by  FP7 MS-BEEM  under Grant Agreement No. FP7-PEOPLE-2013-IEF-625388  and by  COST Action MP1306 EUSpec. 16th  International Conference on X-ray Absorption Fine Structure  ( XAFS16 ) IOP Publishing Journal of Physics: Conference Series 712 (2016) 012002 doi: 10. 1088/1742-6596/712/1/012002.
Introduction The genus Haemophilus  (Winslow et al. 1917 ) was described in 1917  [1]  and currently meningitis, bacteremia, sinusitis, and/or pneumonia  [2]. The current taxonomic classification of prokaryotes relies on a combination of phenotypic and genotypic characteristics  [3, 4] ; including 16S rRNA sequence similarity, G + C content and DNA-DNA hybridization. However, these tools suffer from various drawbacks, mainly due to their threshold values that are not applicable to all species or genera  [5, 6]. With the development of cost-effective, high-throughput sequencing techniques, dozens of thousands of bacterial genome sequences have been made available in public databases  [7]. Recently, we developed a strategy named taxonogenomics in which genomic and phenotypic characteristics, notably the MALDI-TOF-MS spectrum, are systematically compared to the phylogenetically-closest species with a name with standing in nomenclature  [8, 9].
Classification and features In June 2013, a bacterial strain (Table  1 ) was isolated by cultivation on 5 % sheep blood-enriched Columbia agar (BioMérieux, Marcy l'Etoile, France) of a peritoneal fluid specimen obtained from a 44-year-old Senegalese woman who suffered from pelvic peritonitis that had complicated a ruptured ovarian abscess  [10]  and hospitalized in Hôpital Principal de Dakar, Senegal. The strain could not be identified using MALDI-TOF-MS. Strain FF7 T exhibited a 94. 8 % 16S rRNA sequence identity with Haemophilus parasuis strain ATCC 19417 T (GenBank accession number AY362909), the phylogenetically-closest bacterial species with a validly published name (Fig. 1 ). These values were lower than the 98. 7 % 16S rRNA gene sequence threshold recommended by Meier-Kolthoff et al.
e. , a direct report exists in the literature); NAS: Non-traceable Author Statement (i. e. , not directly observed for the living, isolated sample, but based on a generally accepted property for the species, or anecdotal evidence). These evidence codes are from the Gene Ontology project  [34]. If the evidence is IDA, then the property was directly observed for a live isolate by one of the authors or an expert mentioned in the acknowledgements MALDI-TOF protein analysis was carried out as previously described  [12]  using a Microflex LT (Bruker Daltonics, Leipzig, Germany). For strain FF7 T , scores ranging from 1. 32 to 1. 56 were obtained with spectra available in the Brüker database. Therefore the isolate could not be classified within any known species. The reference mass spectrum from strain FF7 T was incremented in our database (Additional file 3: Figure  S1 ).
Genome project history The strain was selected for sequencing on the basis of its 16S rRNA similarity, phylogenetic position, and phenotypic differences with other members of the genus Haemophilus, and is part of a study aiming at using MALDI-TOF-MS for the routine identification of bacterial isolates in Hôpital Principal in Dakar  [10]. It is the eleventh genome of a Haemophilus species and the first genome of Haemophilus massiliensis sp. nov. The Genbank accession number is CCFL00000000 and consists of 46 contigs. Table  2  shows the project information and its association with MIGS version 2. 0 compliance  [13].
The circularized DNA was mechanically sheared to small fragments with an optimal at 569 bp on the Covaris S2 device in microtubes (Covaris, Woburn, MA, USA). The library profile was visualized on a High Sensitivity Bioanalyzer LabChip (Agilent Technologies) and the final library concentration was measured at 24. 42 nmol/L. The libraries were normalized at 2nM Fig. 1  Phylogenetic tree showing the position of Haemophilus massiliensis strain FF7 T relative to the most closely related type strains other type strains (type = T ) within the genus Haemophilus. The GenBank accession numbers for 16S rRNA genes are indicated in parentheses. An asterisk marks strains that have a genome sequence in the NCBI database. Sequences were aligned using MUSCLE  [35] , and a phylogenetic tree inferred using the Maximum Likelihood method with Kimura 2-parameter model using the MEGA software.
Genome annotation Open Reading Frames were predicted using Prodigal  [14]  with default parameters but the predicted ORFs were excluded if they spanned a sequencing gap region. The predicted bacterial protein sequences were searched against the GenBank database  [15]  and the Clusters of Orthologous Groups databases using BLASTP. The tRNAScanSE tool  [16]  was used to find tRNA genes, whereas ribosomal RNAs were found using RNAmmer  [17]  and BLASTn against the GenBank database. Lipoprotein signal peptides and the number of transmembrane helices were predicted using SignalP  [18]  and TMHMM  [19]  respectively. ORFans were identified if their BLASTP E-value was lower than 1e-03 for alignment length greater than 80 amino acids. If alignment lengths were smaller than 80 amino acids, we used an E-value of 1e-05.
Such parameter thresholds have already been used in previous works to define ORFans. Artemis  [20]  was used for data management and DNA Plotter  [21]  for visualization of genomic features. The Mauve alignment tool (version 2. 3. 1) was used for multiple genomic sequence alignment  [22]. To estimate the mean level of nucleotide sequence similarity at the genome level, we used the AGIOS home-made software  [9]. Briefly, this software combines the Proteinortho software  [23]  for detecting orthologous proteins in pairwise genomic comparisons, then retrieves the corresponding genes and determines the mean percentage of nucleotide sequence identity among orthologous ORFs using the Needleman-Wunsch global alignment algorithm. The script created to calculate AGIOS values was named MAGi and is written in perl and bioperl modules.
Insights from the genome sequence Here, we compared the genome sequences of Haemophilus massiliensis strain FF7 T (GenBank accession number CCFL00000000) with those of Haemophilus parasuis strain SH0165 (CP001321), Haemophilus influenzae strain Rd KW20 (L42023), Aggregatibacter segnis strain ATCC 33393 T (AEPS00000000), Haemophilus sputorum strain CCUG 13788 T (AFNK00000000), Haemophilus   As it has been suggested in the literature that the G + C content deviation is at most 1 % within species, these data are an additional argument for the creation of a new taxon  [25]. The gene content of Haemophilus massiliensis is larger than those of H. parasuis, H. influenzae, A. segnis, H. sputorum, H. aegyptius, H. parainfluenzae, H. haemolyticus, H. ducreyi, and H. parahaemolyticus (2,319, 2,299, 1,765, 1,956, 2,072, 2,020, 2,068, 2,056, 1,717, and 1,980, respectively) but smaller than that of H.
Second, non-flow effects (such as resonance decays, jets, etc. [22]) could bias the q 2 measurement. In this work we discuss both aspects in detail, making use of different detectors with different intrinsic resolution and different η coverage. The paper is organized as follows. In Sect. 2 a brief review of the ALICE detector and of the data sample is presented. In Sect. 3 the analysis technique, with an emphasis on the event selection and on the particle identification strategy, is discussed. The results are presented in Sect. 4. Their implication for the hydrodynamic interpretation is discussed in Sect. 5. Finally, we come to our conclusions in Sect. 6.
ALICE detector and data sample The ALICE detector at the CERN LHC was designed to study mainly high-energy Pb-Pb collisions. It is composed of a central barrel (|η| 0. 8 for full-length tracks), containing the main tracking and particle identification detectors, complemented by forward detectors for specific purposes (trigger, multiplicity measurement, centrality determination, muon tracking). A detailed description of the apparatus can be found in  [24]. The main detectors used for the analysis presented in this paper are discussed below. The main tracking devices in the central barrel are the Inner Tracking System (ITS) and the Time Projection Chamber (TPC). They are immersed in a 0. 5 T solenoidal field. The ITS is the detector closest to the interaction point. It is a six-layer silicon tracker with a very low material budget (∼ 7% of one radiation length X 0 ).
The dE/dx, computed as a truncated mean utilizing only 60% of the available samples, has a resolution of ∼ 5% in peripheral and ∼ 6. 5% in central collisions  [26]. At a radius of 3. 7 m from the beam axis, the Time of Flight (TOF) detector measures the arrival time of particles with a total resolution of about 85 ps in Pb-Pb collisions, allowing a π/K (K/p) 2 σ separation up to p T = 3 (5) GeV/c. The ALICE reconstruction software performs tracking based either on the information from the TPC alone (TPC-only tracks) or on the combined information from the ITS and TPC (global tracks). The former have the advantage of an essentially flat azimuthal acceptance, and are used for v 2 and q 2 measurements. The latter provide better quality tracks (σ p T /p T ∼ 1. 5% at p T = 10 GeV/c)  [26] , rejecting most of the secondary tracks.
Acknowledgments The ALICE Collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE Collaboration gratefully acknowledges the resources and support provided by all Grid centres and the  Worldwide LHC Computing Grid  (WLCG) collaboration. The ALICE Collaboration acknowledges the following funding agencies for their support in building and running the ALICE detector:  State Committee of Science, World Federation of Scientists (WFS)  and  Swiss Fonds Kidagan ,  Armenia ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Financiadora de Estudos e Projetos (FINEP) ,  Fundacão de Amparo à Pesquisa do Estado de São Paulo (FAPESP) ;  National Natural Science Foundation of China (NSFC) , the  Chinese Ministry of Education (CMOE)  and the  Ministry of Science and Technology of China (MSTC) ;  Ministry of Education and Youth of the Czech Republic ;  Danish Natural Science Research Council , the  Carlsberg Foundation  and the  Danish National Research Foundation ; The  European Research Council  under the European 23 Event shape engineering in Pb-Pb collisions at √ s NN = 2.
On the German side, the main issue is to reintroduce the awareness that important floods could occur in the region. In fact, the position of the oldest highest flood marks seems to show that the correction of the main rivers (XIX th century) allows to decrease the number and the severity of modern floods. This engineering work and the feeling that floods can be controlled induce a misperception of this risk by the population and the stakeholders. 4 )  [34, 35]. The database supplies all the information needed to implement a "risk culture". Citizens and local authorities can also contribute to supply this database by introducing their own information, experience, feedback on past / modern flood events. The ORRION database ("Regional Observatory of Flood Risks", cf. 3rd part) was designed and offers the opportunity to the citizens and stakeholders to contribute to the emergence of a common c TRANSRISK (analyse interdisciplinaire et transfrontalière de l'histoire des risques d'inondation dans l'espace du Rhin supérieur): http://www.
agence-nationale-recherche. fr/projetanr/?solr=run&tx_lwmsuivibilan_pi2%5BCODE%5D=ANR-07-FRAL-0025 d TRANSRISK² (Transnational flood risk management in the Rhine Bassin : A historic-progressiv approach): http://www. agence-nationale-recherche. fr/?Project=ANR-13-FRAL-0012 transmission of past events, on both side of the Rhine River Graben. 3 The online and participative database: ORRION 3. 1. From the TRANSRISK database to ORRION TRANSRISK database was the first step of the implementation of an online and participative database. The main information that is now available in ORRION arises from the TRANSRISK database and the first challenge was to input all the information in a same database. The data on all historic floods that occurred in the Rhine Graben (characteristics, intensity, management, etc.
) were gathered by a precise analysis of archives documents and spread over a period between 1480 and 2015  [36]. These data encompass all the rivers of Alsace (French side) and Bade-Württemberg (German side) as well as the Rhine River. Information concerning the Moselle and Sarre were recently added to this database. The reason is fourfold: to study more precisely the behavior of rivers located on the both sides of the Vosges mountains (western slopes are more exposed to heavy rainfall (Lorraine side) while eastern slopes are leeward (Alsace side), to evidence rivers behavior similarities or disparities between western slopes of Vosges and Black-Forest mountains, to introduce an additional cross-border risk management analysis: the Moselle and the Sarre Rivers are flowing in France, then in Germany, to adapt our analysis to the administrative subdivisions: the Rhine-Meuse ("Agence de l'Eau Rhin-Meuse") or the "Grand Est" region are both in charge of the floods risk management and ORRION database could then cover the same territory.
The database contains more than 3. 000 events with a variety of gravity and extension (some major events concern simultaneously the Rhine and its French and German tributariesi. e. 1480, 1651, 1778, 1876, 1882, 1910, Fig. 4 ). We are aware that the TRANSRISK database is a researchers' tool. That is to say that it is designed by researchers and for research, without any operational objectives. The second challenge of the transposition of the TRANSRISK database to ORRION consists in the information's accessibility for all users (citizens and stakeholders) and in its operational effectiveness.
Observatories and online databases on diachronic information: state of the art This online and participative ORRION database is operational since the beginning of 2015 and all the reflections on its design have been conducted in 2014. To create this database and its structure, we undertook a state of the art of all the existing databases on flood risks, such as: the National Observatory of Natural Risks (ONRN e ); the database on the Historic Flood Events (BDHI f ) done in the continuation of the European "Flood" Directive; the database produced by the RTM g services ("Restauration des Terrains en Montagne") focused on the Alpine and Pyrenean regions. These databases are all conducted by state services at a national level. It sometimes dilutes the regional information and does not necessarily take into account the local particularities (especially for the Rhine Graben region when the available data are in German language).
Assuming a regional point of view, the ORRM h observatory in Provence -Alpes -Côte d'Azur is interesting. But the web interface to reach information is often rather complex and little attractive. Furthermore, the "collaborative" aspect is only focused on the participation of the government services, restricting the citizens' appropriation of this tool. The Floods Observatory of Franche-Comté contains a huge amount of data, localized information, endowed with an accessible and attractive interface, but no direct contributions are allowed. The appropriation by the population and the localization of all the data are the two main points for ORRION. The lack of documentation on extreme flood events and problems due to the historical context of Alsace can be overcome thanks to the population's contributions.
Presentation of the ORRION website's design The ORRION website is totally funded by the French-German ANR-DFG TRANSRISK² program. Its design is built as a user-friendly, easy and shared tool (Fig. 5 ). Its structure doesn't lean on a "classic" online database (to minimize all the management difficulties) but is built on a blog structure (WordPress type). All information concerning a specific flood corresponds to one entrance in the blog, with a specific indexation, allowing a research by keywords. The huge amount and the complexity of the information contained in the TRANSRISK database will probably require several years before the entire data will be available on the ORRION website. The ORRION website interface will progress in the next months in its presentation and in its contents. Firstly, the site is intended to inform and to share floods' information in the particular context of a cross-border territory.
This is why the site presents a bilingual character (French and German) and should be, in the future, trilingual by adding a dialectal version in Alsatian (to increase the participation of older people who are the "holders" of the memory for past events). Secondly, ORRION website will also gather data on other natural disasters such muddy floods and snow avalanches (with the development of the site ORRIA: regional observatory of the risks of avalanches in Alsace). ORRION will also be developed on other territories: it will be a trinational tool (including information on the historic floods in the cantons of Basel in Switzerland). Finally, from 2017, ORRION will supply local information on the status of the administrative procedures (PPRI, PCS, etc. ) as well as the factual, technical and legal watch (on the reference model of the « Institut des Risques Majeurs i »).
Online and participative website: limits and main issues The launch of ORRION website went along with several communications actions, such as articles in the regional newspapers, specific newsletters sent to local stakeholders, distribution of flyers in specific sectors in Alsace (such as sectors involved in administrative procedures to manage floods). All these communication actions had at the same time interesting and frustrating feedbacks. They allowed us to gather new information, especially by the local "History Societies" (which organized events in 2016 related to the launch of ORRION website) but direct inputs on ORRION were very scarce. This can be explained by the age of the contributors (50 years old and more in average). Oldest people are not always familiar with online databases and prefer providing their personal data to local stakeholders or ORRION designers leaving them the responsibility to input the information on the website.
A pedagogic and educational dimension was then given to the usefulness of historic data in current flood risks calculation and indirectly flood risk management policies. These collaborations between local stakeholders and ORRION's researchers led the strengthening of the development of a "risk culture" in a context of floods' unfamiliarity and lack of citizens' interest for this question. Information on historical floods extracted from the ORRION database were recently used within a partnership between the ORRION's researchers, MAYANE j and a local risk management stakeholder (DDT -"Direction Départementale du Territoire", Bas-Rhin). This collaboration illustrate how important are information and communication campaigns before the fulfillment of the PPRI (Bruche River, Ill River, Moder River, Giessen-Liepvrette Rivers, Fig.