dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical calculations All calculations, statistics, network statistical analyses were performed using in-house python scripts, with the Numpy, Scipy, Biopython and Pandas python-packages. vConTACT is implemented in python with the same dependencies. The tool is available at https://bitbucket. org/MAVERICLab/vcontact. Scripts used in the generational and calculations of data are available at https://bitbucket. org/MAVERICLab/vcontact-SI.
Network visualization and analysis The network was visualized with Cytoscape (version 3. 1. 1; http://cytoscape. org/), using an edge-weighted spring embedded model, which places the genomes or fragments sharing more PCs closer to each other. Topological properties were estimated using a combination of python and the Network Analyzer 2. 7 Cytoscape plug-in  (Assenov et al. , 2008).
RESULTS AND DISCUSSION vConTACT analytical workflow and terminology The vConTACT analyses are based on previously established gene sharing network methods  (Lima-Mendez et al. , 2008). Briefly, PCs are established across all genomes in the dataset; with vConTACT doing this by default using MCL clustering from all-versus-all BLASTP comparisons (though user-specified clusters can also be used). PC profiles of genomes or genome fragments (herein 'genome') are then calculated, where the presence and absence of PCs (from the entire PC dataset) along a genome are established and then compared pairwise between genomes (Fig. 1 ). The pairwise genome comparisons are then mathematically adjusted (using the hypergeometric similarity formula) to establish a probability that any genome pair would share n PCs, given the total number of all PCs.
This probability is log-transformed (in similar fashion to BLAST E-values) into a significance score and applied as a weight to an edge between the two paired genomes in a similarity network. High significance scores represent a low probability that two genomes would share n PCs by chance, which can be interpreted as evidence of gene-sharing and presumably evolutionary relatedness between the paired genomes. After evaluating all pairings in the dataset, significance scores ≥1 are retained, and a network of the remaining genome pairs is constructed. MCL is subsequently applied to identify structure in the gene sharing network, but now the clusters represent groups or related genomes and are termed viral clusters (''VCs''). MCL is also applied against the network of PCs, whose members can be similar to members of other PCs.
Since both genera (Cp8virus and Cp220virus) are distantly related to the Tevenvirinae  (Javed et al. , 2014) , displaying only ∼11% shared genes to other Tevenvirinae (an average weight of 18. 5) and ∼6% (11. 8), respectively (Tables  S2  and S3 ), these groupings might be driven by the fact that only 2 reference genomes (i. e. , Campylobacter phages CPX and NCTC12673) are available in our ViralRefSeq dataset for Cp220virus. To test this, we artificially doubled the number of the genomes for this group by adding their replicas (phages CPX_copy1 and NCTC12673_copy1, Table  S4 ) to the network. For all edges between the replicas and original genomes and outside them, vConTACT recalculated the weights. This led the Cp220virus genomes to clearly separate from VC1 and instead be correctly placed alongside VC 87 (Table  S4 ).
vConTACT, an iVirus tool for network-based viral taxonomy Given the strong and robust performance of these network classification methods  (Lima-Mendez et al. , 2008)  to largely capture known viral taxonomy from genomes alone, we sought to democratize the analytical capability. To this end, we developed a tool named ''vConTACT'' (overview of its logic in Fig. 1 ) and integrated it into iVirus, a virus ecology-focused set of tools also known as ''apps'' and databases  (Bolduc et al. , 2016). Such implementation at iVirus enables any user to run the application simply by providing viral sequences (including novel and/or reference sequences) alongside a CSV-formatted file containing gene and sequence information with all compute, storage and data repository happening via the CyVerse cyberinfrastructure (formerly the iPlant Collaborative  (Goff et al.
Limitations and future developments of vConTACT Since vConTACT uses a genome similarity network, it displays the extent of shared genes between genomes as edges, but not what the shared genes are  Corel et al. (2016). This lack of information on the identity of shared genes (i. e. , host-related genes and ancestral viral genes) in the graph makes the biological interpretation of network connections difficult, and can lead to a misunderstanding of genome evolution (i. e. , T5virus) when using topology to detect the chimeric viruses. Additionally, the limiting resolution of MCL in poorly-sampled regions of and/or highly-overlapped viral genomes cannot uncover their hidden substructure (i. e. , Cp8virus and mycophages, respectively). These particular types of limitations had not been reported previously, likely because of the smaller dataset available at the time.
The Generalized Aerosol Retrieval from Radiometer and Lidar Combined data algorithm (GARRLiC) and the LIdar-Radiometer Inversion Code (LIRIC) provide the opportunity to study the aerosol vertical distribution by combining ground-based lidar and sun-photometric measurements. Here, we utilize the capabilities of both algorithms for the characterization of Saharan dust and marine particles, along with their mixtures, in the south-eastern Mediterranean during the CHARacterization of Aerosol mixtures of Dust and Marine origin Experiment (CHARADMExp). Three case studies are presented, focusing on dust-dominated, marinedominated and dust-marine mixing conditions. GARRLiC and LIRIC achieve a satisfactory characterization for the dust-dominated case in terms of particle microphysical properties and concentration profiles.
Acknowledgements. The research leading to these results has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme ACTRIS-2  (grant agreement no. 654109 ). The work has been developed under the auspices of the  ESA-ESTEC  project " Characterization of Aerosol mixtures of Dust And Marine origin " contract no. IPL-PSO/FF/lf/14. 489. The work was also supported by the  European Research Council  under the European Community's  Horizon 2020 research and innovation framework programme/ERC  grant agreement 725698 (D-TECT). The publication was supported by the  European Union 's  Horizon 2020 Research and Innovation programme  under grant agreement no. 602014 , project  ECARS (East European Centre for Atmospheric Remote Sensing ). The authors acknowledge support through the  Stavros Niarchos Foundation.
Meshing Next comes the meshing and the reconstruction of the computed 3D shape. This task was achieved using Meshlab  [25]  which is an advanced 3D software system for the processing and editing of unstructured 3D triangular meshes. Our aim is to regularize surfaces and reduce noise and other artificats, thus our choice focused on the Poisson surface reconstruction algorithm proposed in Meshlab. The method requires the 3D points cloud as well as their corresponding normals. The computation of the normals requires 3 steps:  "Locating tangent planes": makes it possible to find the tangent planes for the approximation of the local surface. For this, the center of gravity of each vertex of the triangles constituting the mesh is computed as the average of the set of the K-closest neighbors.  "Building Riemannian graph": enables to construct the graph of Riemann, which helps creating the structure, where for each vertex, the borders of its closest K-neighbors are included in the construction.
However, each of them suffers from difficulties, such as finding the appropriate class, defining parameters or applying pre-or post-treatments to fuse parts or remove noise  [17]. Furthermore, it is unlikely to obtain the exact subpart targeted: even if the most appropriate algorithm for the current model and application is found and its parameters adjusted, unwanted points will still be included and others missed. Using geometry information describing the object to select may overcome some of the aforementioned limits  [16] ; however, it is only useful to identify objects already known by the algorithm or the database. Alternatively, interactive approaches have been developed. The simplest ones consist in pointing techniques  [13] , but they are not suited to the task because selecting points one by one becomes tedious when facing millions of them.
The colour code used in this mode is green for the points that are selected and blue for the others. Additional colours (red and white) were used exclusively for the experiment and are described in the users experiment section (section 5. 1. 1). The only interactions offered in this mode is to rotate the camera and to change the mode to "edit". Using the Worm Selector: View mode of the application used during the user's experiment. The edit mode (Figure  8 ) supports the visualization and manipulation of the drawing-plane. The colour code used in this mode is the same than in the previous mode. However, to help the user performing an accurate selection, this colour code is only applied to the points situated on one side of the drawing-plane and below a user's adjustable distance to the drawing plane.
Drawing plane Contour being drawn slices of data along one given axis. The contours can thus be defined on each parallel slices, then linked to obtain a free form envelope. As opposed to this approach, when using the Worm Selector, contours can be drawn in planes that are not necessarily parallel planes. The goal of the first experiment is thus to establish whether drawing contours on non-parallel planes makes the use of a contour based selection technique more complex or not. To this end we implemented a second interaction technique, named Parallel Contours Selector. Section 6 describes in detail this technique and the experiment. The goal of the second experiment is to compare the Worm Selector to the most widely used type of selection technique in the 3D domain (see Section 2 or existing software such as CloudCompare  [1] ): a selection based on a simple and predefined 3D volume.
A) Apparatus In both cases the experiment was performed on a PC running Ubuntu 14. 10 64 bits, with one AMD FirePro M4000 graphics card. One display output was connected to a 24 inches screen (1980 × 1080 resolution). A keyboard and a simple mouse with two buttons and a wheel were used as inputs. The volume selection takes place in a 3D scene made of a simple cube of points and displayed in a 3D grey environment. For the two experiments the 3D scene can only be rotated around its centre, no translation or zoom were supported. The point cloud has been manually generated. It is made of points that are regularly distributed in space, with a small random shift avoiding a too perfect alignment that would affect the perception. During the experiment, we extended the colour code used in the initial integration of the Worm Selector in an application (see Section 4).
The Telescope Bernard Lyot. The first case study relies on a collaboration with the Telescope Bernard Lyot (TBL) situated on top of the Pic du Midi. The TBL is part of the Observatoire Midi-Pyrénées (OMP) research lab and is affiliated to the French National Research Center (CNRS) and the University of Toulouse. In order to create a database of the blueprints of all its buildings and to store a copy of their current state, the University of Toulouse has initiated a vast campaign in order to 3D scan all its buildings. The entire platform of the Pic du midi has thus been 3D scanned from the outside, including outside of the TBL (Figure15-left). The inside of the tower in which the TBL is situated has also been 3D scanned to obtain a 3D point cloud of the telescope itself (Figure15-right). This point cloud contains over 330 million of points.
8. 1 1 The Telescope Bernard Lyot. The first case study relies on a collaboration with the Telescope Bernard Lyot (TBL) situated on top of the Pic du Midi. The TBL is part of the Observatoire Midi-Pyrénées (OMP) research lab and is affiliated to the French National Research Center (CNRS) and the University of Toulouse. In order to create a database of the blueprints of all its buildings and to store a copy of their current state, the University of Toulouse has initiated a vast campaign in order to 3D scan all its buildings. The entire platform of the Pic du midi has thus been 3D scanned from the outside, including outside of the TBL (Figure15-left). The inside of the tower in which the TBL is situated has also been 3D scanned to obtain a 3D point cloud of the telescope itself (Figure15-right).
Concluding remarks In this paper, we introduced different ways of constructing a SHAKE function based on an inner function RawSHAKE and a parameterizable tree of nodes. Resulting modes are Sakura-compatible and lead to interesting asymptotic bounds with regard to the memory usage in a sequential implementation, the ideal parallel time and the number of involed processors. We showed new directions for tree hash modes with a focus on space-time trade-offs, whether these modes are intended for stored content or for streaming live content. We think that it would be of interest to propose variants of our proposed modes using various optimizations suitable to software implementations. On the accuracy of complexity measures. This paper has focused on the asymptotic analysis (using big-O) of tree hash modes but there is also an interest for their optimizations at finite distance (i.
[10] [11] [12]. We develop a novel method to analyse spatiotemporal variations between trajectories and use an unsupervised clustering method to quantify similarity between surgical instrument motions. With such method, we want to analyse if similar manipulability is required for the design of HMIs dedicated to surgical training, in contrast to perioperative ones. The remaining sections are organized as follows. In Section 3, we present the robotic setup and the novel contactless control approach, as well as the methodological pipeline from the user hand tracking to the robot control. Section 4 presents both the dataset acquired during the surgical training task and the metrics. Additionally, we describe a complementary evaluation based on a novel unsupervised spatiotemporal trajectory clustering for a quantitative analysis of surgical tool motions.
Surgical Robot Control The robot (or slave side) is visible at the right in fig. 2. The RAVEN-II robot is an open-source platform dedicated to research in robotic telesurgery  [16]. Composed of two robotic arms, actuation of the surgical tools is achieved by 8 motors combined with a cable-driven mechanical architecture for a lighter structure. Each surgical tool allows 7 DoFs at the tooltip. The control software runs on top of the Robotic Operating System (ROS) middleware, on a real-time Linux kernel (Ubuntu 10. 04 LTS with RT-Preempt patch). Data computed from the console are directly filtered using a lowpass filter. For this purpose, the filter was tuned with respect to previous studies addressing human motion characteristics  [39]  and human motion filtering  [3, 40]. The cut-off frequency has been set to 1.
Evaluation Study To compare both HMIs for robotic training, three operators were asked to execute a predefined surgical training task on the robotic platform presented in Section 3. By recording the surgical tool motions during the ex-ecution of the task, a dataset of trajectories is created for both interfaces (Section 4. 1). From these trajectories, six metrics are computed to quantify performances for each interface (Section 4. 2). Additionally, an unsupervised clustering approach is proposed to both analyse spatiotemporal differences between tool trajectories and provide a score describing not only global performance but local deviations also (Section 4. 3).
Trajectory Dataset A dataset of trajectories was acquired for both the Sigma. 7 interface and the Leap Motion. For each dataset, the same training task was executed five times by three different participants with different skill levels: an urologist expert who regularly performs surgeries with the da Vinci system (named 'C'), a last year resident who only used few times a robotic training system (named 'B') and a teleoperation system engineer (named 'A'). All participants were right-handed. The objective behind integrating various experience into the experimental protocol is to analyse if the surgical experience affects the HMI comparison. Before experiments, participants were asked to sign consents regarding the study and were all briefed about the setup, its specificities and the task to ensure that they would perform it appropriately and consistently.
This work was supported in part by the  French ANR  within the  Investissements d'Avenir Program (Labex CAMI ,  ANR-11-LABX0004 ); by the  Equipex   ROBOTEX Program  ( ANR-10-EQPX-44-01 ); and by the  Région Languedoc-Roussillon. Fabien Despinoy( ).
He is an associate member of Lab-STICC in the WSN unit of Professor Bernard POTTIER, in Brest. * Corresponding author. Email: eloi. keita@laposte. net propagation simulation in urban environment, in a distributed manner. The key point of this work is a cellular representation of the city in order to model streets, gardens, ring roads, buildings, and rivers. Geo referenced image analysis, complemented by database consultation, for example to retrieve elevation data, generates this cellular model. Then, this model is converted into a system made of interconnected processes, that can reproduce many collective behaviors, whether physical or digital. We have developed a cellular automata that models sound propagation, including reflection and refraction, working on a graphics accelerator. Finally, we have produced a coupling method between observation systems by sensor networks and physical systems.
Physical propagation simulation approach We have chosen a cellular model proposed by Radu and Ioana Dogaru in  [7]. The proximity to the cellular automaton being used is a Von Neumann radius 1 with neighbourhood cells consisting of North, South, East and West. The variables of state of the cells include the presence of buildings, one or more source (s) sound (s), physical parameters specifying a sound. The transition function operates on the parameters of mitigation and diffusion of the sound wave, by reconstructing the local developments thereof. A practical realization has been constructed, including cellular systems integrating data from an image extracted from navigation on OpenStreetMap in the city of Brest. Each pixel is associated with a zone of a few meters wide depending on the scale of the map, eg 45m wide for each pixel for the first simulation shown Figure  9.
Results A global C. fetus collection. To investigate the population structure and genomic evolution of mammal associated C. fetus we whole-genome sequenced 177 C. fetus strains isolated from 13 different countries and 5 different hosts, and combined our data with 11 published genomes sampled from 4 additional countries, giving a total data set of 188 genomes coming from strains isolated from 1952 to 2015 (63 years). To confirm species membership, the average nucleotide identity (ANI) was calculated for all possible pairs of genomes  10. This analysis revealed a group of 6 genomes with an ANI <95% compared to the other genomes (two public and four sequenced as part of our study), which were assigned to the subspecies Cft due to high genomic divergence and were therefore removed from subsequent analysis.
fetus, we compared all genomes (n = 182) to the Human Pan-Microbe Communities database  28. This allowed us to scan 7121 shotgun metagenomic data sets representing an international sampling of human feces. Interestingly, C. fetus was detected in ~8% of the samples from healthy humans. In contrast, the commensal Escherichia coli was detectable in 7% of the samples and the human pathogens C. jejuni and C. coli were detected in less than 0. 5% of the samples. Of the 10 C. fetus genotypes detected in the gastrointestinal microbiota of healthy individuals, 22. 11% belonged to strains from the modern bovine lineage and 77. 89% belonged to strains from human lineages (Fig. 4a ). No correlation was observed with gender, sex, age, ethnicity, or geographical origin of the donor or originating sample.
Methods Sequencing, species typing, and pan-genome. Strains were retrieved from independent collections over the world (information of each sequenced strain can be found in Supplementary Data 1). Genomic DNA was prepared and sequenced using the Illumina Hi-Seq platform with library fragment sizes of 200-300 bp and read length of 100 bp at the Wellcome Trust Sanger Institute, as previously described  34. Each sequenced genome was de novo assembled and improved using an in-house pipeline developed at the Wellcome Trust Sanger Institute  35. Briefly, raw reads are first screened with Kraken 36 software in order to discard significant amounts of contaminating reads from other organisms. Data summary statistics are then generated using FastQC (www. bioinformatics. babraham. ac. uk/projects/ fastqc).
Then, reads are assembled using Velvet  37  to generate multiple assemblies by varying the k-mer size between 66 and 90 % of the read length using with VelvetOptimiser  38. From this set of assemblies the one with highest N50 is chosen. Iterative assembly improvement steps are subsequently run to scaffold the contigs using SSPACE  39  and to fill in sequence gaps using GapFiller  40. Finally, contigs are excluded from the assembly if they are shorter than the target fragment size (normally 300-500 bases). Remaining contigs are sorted by size and renamed in a standardized manner to include the raw sequencing data accession number. To assess the quality of the assembly and to produce summary statistics, the reads are aligned back to the final assembly using SMALT  41. The assemblies are then automatically annotated using Prokka  42  with a genus-specific database from RefSeq.
The resulting annotated assemblies are in GFF3 format. A more detailed explanation of parameters used for each software at each step can be found in Page et al. 35  The membership to the species C. fetus was assessed by comparing each assembled genome against each other and the published genomes using the ANI, as previously implemented  43. The annotated assemblies in GFF3 format were input to Roary, a tool that builds large-scale pan-genomes, to construct the core and accessory genomes  44. Briefly, coding sequences are extracted from annotation files and converted into protein sequences, filtered to remove partial sequences and iteratively pre-clustered with CD-HIT  45. Then all-against-all comparisons of pre-cluster representatives are performed with BLASTP using an user defined percentage sequence identity cutoff.
Additionally, to check the core genome quality we inspected that the housekeeping genes belonging to the C. fetus MLST scheme clustered in the corresponding homologous groups. Phylodynamics, population structure, and host jump analyses. The core genome alignment extracted with Roary (1,098,169 aligned positions representing 980 concatenated single copy genes) was filtered using Gubbins  47  to remove high SNP density regions which indicate putative recombination events (44,213 positions removed representing ~4% of the core genome alignment). We used the BEAST v1. 7. 5 package  48  to jointly estimate the substitution rate, a dated phylogeny and the reconstruction of host ancestral states using a continuous-time Markov chain (CTMC) discrete model. Briefly, we tested various combinations for the molecular clock prior (strict vs.