dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Acquisition The TLS survey was carried out by the Photogrammetry and Geomatics Group (INSA Strasbourg) on 1 December (some complementary scans were taken on the next day, after scaffolding disassembly). A total of six scans were taken from the bottom of the empty lock, and eight from the top, using the Faro Focus 3D X330. Both configurations are illustrated in Figure  4. We used spherical targets (as the one that is visible in Figure  5 ), distributed around the structure, to facilitate the registration of the point clouds. The coordinates of the sphere centres were established using traditional survey methods based on a set of reference points implemented on the site in a local coordinate system. A sample model of the top structure of the lock is shown in Figure  5b. It can be noticed that, since there was a little bit of water left at the bottom of the lock chamber, the latter could not be modelled.
To alleviate this problem, one solution is to mesh the reference cloud, which we do with Technodigit's 3DReshaper software (Neyron, France, www. 3dreshaper. com). Finally, the cloud-to-mesh comparison is performed with CloudCompare (www. cloudcompare. org), which uses an algorithm inspired from Metro  [20]. In practice, since no reference is available for the floor of the lock, the comparison is carried out for each wall separately. This study was performed before and after correction of the sonar tilt angle. First, a spatial visualization of the distance distributions between the sonar point cloud and the meshed TLS model is proposed in Figure  15  before tilt angle correction, and in Figure  16  after it. While a systematism is apparent in the distance distributions before correction in the form of repetitive patterns, it is attenuated afterwards: the results are more uniform.
• The integrated longitudinal and lateral autonomous is considered in order to cover lane change maneuver. Using TS fuzzy modeling to represent the lateral vehicle dynamic as in  [18] , the proposed H ∞ lateral controller can handle a large variation range of vehicle speed. This approach reduces the design conservatism. The paper is organized as follows. Section 2 is dedicated to the longitudinal control design of autonomous vehicle while the lateral control one is proposed in Section 3. Section 4 is devoted to simulation results and shows the impact of wireless communication in individual and string stability using the CarSim software package. Finally, some conclusions have been made and future works will be highlighted.
CarSim Software Validation CarSim is a professional software dedicated to the simulation of the vehicle dynamics, and it is developed by the 'Mechanical Simulation Corporation' company. With this simulator, any vehicle's driving test on a test track or on the road can be simulated before the actual real test. Thus, we can virtually reproduce different driving situations and test the behavior of the vehicle and its reaction to different maneuvers (lane change, slalom, acceleration, slope, etc. ). CarSim has five main parts that allow you to choose the simulation parameters, the test conditions and the animation as well as the illustration of the results:.
• Vehicle Parameters: This block is used to define several physical parameters of the vehicle (dimensions, engine, tires, bodywork and mathematical models that represent the tire/ground contact forces and suspension forces exerted on the vehicle, etc. ). CarSim has a standard interface to Matlab/Simulink allowing the co-simulation between them. Thus, the platooning approach is verified by CarSim in an obstacle avoidance scenario (Figure  12g ). A double lane change test (Figure  12h ) is conducted with steering wheel angles depicted in Figure  12c. In this simulation, the vehicles are driven at a variable speed (Figure  12b ). We can see from the results shown in Figure  12a -g the good performance and efficiency of the proposed approach despite communication delays. In fact, the controlled vehicle managed to follow its preceding vehicle (leader).
Gene expression profiling by RT-qPCR Total RNA was extracted from the cells using TRIzol Reagent (Invitrogen # 15596026) and RNeasy Mini Kit (Qiagen # 74104, Mississauga, ON, Canada) according to the manufacturer's recommendations. Starting from 0. 5 μg of RNA, cDNA was synthetized using RT2 First Strand Kit (Qiagen # 330404). We designed a custommade RT2 Profiler PCR Array with validated primers directed at 87 genes implicated in EV biogenesis and activity (Table  S1 ). In combination with RT2 SYBR Green Mastermix (Qiagen # 330503), the expression profiles of these genes were quantified simultaneously using RT-qPCR machine (Roche Light Cycler 96) following the manufacturer's recommendations. The results were analysed using Excel and a heat map was created using MeV software. Quantitative PCR of GFAP mRNA was performed in a total reaction volume of 25 μl in 96-well reaction plates using One Step RT-PCR kit (Qiagen # 210210) and 250 nM of the following primers: GFAP (Forward 5ʹ-cgatcaactcaccgccaaca-3ʹ Reverse 5ʹ-gggt gagtttcttgttagttgg-3ʹ), GAPDH (Forward 5ʹcgatcaactcacggccaaca-3ʹ Reverse 5ʹgagtcaacggatttggtcgt-3ʹ).
The raw data were converted into *. mgf format (Mascot generic format) searched using Mascot 2. 3 against human sequences (Swissprot). The database search results were loaded onto Scaffold Q + Scaffold_4. 4. 8 (Proteome Sciences) for spectral counting, statistical treatment and data visualization. We also performed a functional enrichment analysis using FunRich21, an open-source software developed for the analysis of data from EV-specialized databases including Vesiclepedia and Exocarta. The open-source platform GeneMania Cytoscape was used for representation of changes in functional clusters of EV-associated proteins released from mesenchymal and proneural GSCs in their undifferentiated and differentiated states.
Differential expression of vesiculation-related genes by molecular subtypes of GSCs The recent evidence for molecular and functional heterogeneity among GSCs, and the existence of their PN and MES subtypes raises the possibility of a corresponding diversity among EV-mediated cell-interactive networks  [17, 36]. To glean some related insights from the existing GSC gene expression datasets (GEO GSE67089), we extracted mRNA levels of 90 genes including known EV regulators, markers, effectors and selected cargo proteins and we clustered them according to their PN or MES GSC signatures. Interestingly, such supervised clustering analysis (Figure  1 ) shows marked differences between the expression profiles of EV-related transcripts in a series of PN and MES GSC lines. Thus, several such genes were preferentially upregulated in MES GSCs, including ARF6, RAB27A, ANXA11, STX7 relative to PN GSCs (Figure  1  red arrows).
These patterns changed markedly upon induction of cell differentiation with considerable upregulation of CD63 in the 100K fraction of PN DIFF-derived EVs and concomitant depletion of other markers. In the case of MES DIFF cells, we observed strong expression of CD9, CD63, CD81, Syntenin-1, Flotillin-1 and TSG101, while RAB6B and CD82 were not readily detectable (Figure  6 ). This global quantitative marker distribution does not preclude the existence of distinct EV subsets with alternative characteristics within the GSC secretomes. For example, while the total levels of CD63 in the EV fraction of PN GSCs were exceedingly low, as measured by Western blotting (Figure  6 ), subsets of CD63-positive EVs were readily detectable in this material using immuno-Cryo-TEM (Figure  5(c )) or nano-flow cytometry (data not shown).
Similarly, auditing EV proteome for molecular function suggested a link to signal transduction and cell communication in the case of PN GSC, PN DIFF and MES DIFF cells, while EVs from MES GSCs were enriched for proteins linked to nucleic acids and proteins metabolism (Figure  8(c-d )). Using the GeneMANIA application of Cytoscape software  [42] , we were also able to build interactive networks of proteins pathways for GSC (Figure  9(a-b )) or DIFF cells (Figure  9(c-d )), both PN and MES subtypes. The results show that PN GSC-derived EV proteins mainly cluster around networks involved in proteins folding and in cellular/extracellular matrix organization (Figure  9 (a)), while MES GSC-derived EVs are enriched for proteins that play a role in protein translation and nucleic acids processing with an increase of proteins derived from the nucleus (splicesome complex, nuclear proteins) (Figure  9(b) ).
It remains unclear what molecular components of EVs define their uptake or endothelial stimulating activity, but we did not observe any discernable contribution of VEGF or VEGF signalling pathway to these responses (data not shown). Although our experiments demonstrate, in principle, that the heterogeneity of GSC subtypes compounded by their differentiation states may shape the molecular repertoire and biological activity of EV populations, much remains to be studied. For example, the molecular mechanisms involved at different steps of EV regulation in GBM cells (EV biogenesis, cargo assembly, uptake and angiogenic activity) are of great interest and are presently unknown. Whether and how different pathways of GSC differentiation have an impact on EV profiles of the resulting divergent cell types e.
Figure1. Expression of vesiculation-related genes in datasets of glioma cell lines and stem cell lines. Representation of 90 genes of which 87 are known to regulate EV biogenesis, or contribute to EV cargo is shown in TableS1. The remaining genes are elements of the GBM landscape. This information was extracted from transcriptomes of 15 glioma cell lines, including 10 glioma stem cell isolates (GSC) (6 PN, 4 MES) and 5 established GBM cell lines (GBM). The hierarchical clustering shows differential expression of genes based on stem cells subtypes. Blue arrows represent upregulated genes in PN GSC, while red arrows represent upregulated genes in MES GSC.
Comparison Against Other SMT Solvers In this section, we compare our implementation of enumerative instantiation in CVC4 against another state-of-the-art SMT solver: Z3  [14]  (version 4. 5. 1) which, like CVC4, also relies on E-matching instantiation for handling unsatisfiable problems. Before making the comparison, we first summarize the main differences between Z3 and CVC4 here. Z3 uses several optimizations for E-matching that are not implemented in CVC4,  including the use of code trees and techniques for applying instantiation incrementally during the CDCL(T ) search (see Section 5 of  [13] ). It also implements techniques for removing previously considered instantiations from its set of known clauses (see Section 7 of  [13] ). The main advantage of CVC4 with respect to Z3 is its use of conflictbased instantiation c  [28] , which is enabled by default in all strategies we considered.
Artifact We have produced an artifact  [27]  to reproduce the experimental results presented in this paper. The artifact contains the binaries of the SMT solvers CVC4 and Z3, the benchmarks on which they were evaluated, and the running scripts for each configuration evaluated. Detailed instructions are given to perform tests on the various benchmark families with all configurations within the time limits, as well as for retrieving the respective results in CSV format. The artifact has been tested in the virtual machine available at  [21].
Another direction is lifting the techniques seen here to reasoning in higher-order logic. To handle quantification over functions it is often necessary to enumerate expressions, and so performing such an enumeration in a principled manner is paramount for this domain. Techniques from syntax-guided function synthesis  [1]  could be combined with enumerative instantiation to pursue this goal. European Research Council (ERC) starting grant Matryoshka (713999). We would like to thank the anonymous reviewers for their comments. We are grateful to Jasmin C. Blanchette for discussions, encouragements and financial support through his ERC grant.
Manual Outlining of Individual Grains All the slices were ground up to 4000 SiC paper, and the final polishing was performed with a 3 µm diamond suspension. The solidification structure was revealed by warm etching in a supersaturated picric acid aqueous solution (Bechet-Beaujard reagent). Micrographs were taken with a Axioplan 2/Axiocam MRc5 optical microscope (Carl Zeiss, Oberkochen, Germany). The motorized platform combined with Axiovision software (version 4. 6) allowed macroscopic tile images to be created. The freeware Fiji was used for image analysis  [23]. Manual identification of each 2D grain was performed after histogram equalization. Orientations of the dendrite primary axis and secondary arms were used to outline one grain on the first slice. Once a grain was outlined on its first slice of appearance, primary axis and secondary axis of the same grain were once again used as indicators for outlining on the other slices.
3D Reconstruction A program was written in Python 3 to reconstruct each grain in 3D as shown in Figure  3. After retrieving the data per grain from Fiji (as shown in Figure  3a ) using the read-roi functions developed by Mary et al. [24] , each 2D contour belonging to the same 3D grain needed to be discretized. As the main goal is surface reconstruction, polar discretization was done on each contour with the same predefined array of angles. Henceforth, each angle is represented as a line, which is depicted in Figure  3b. If random points were taken on two successive contours, vertices could cross each other, and the surface would not be properly reconstructed. The chosen grain in Figure  3  is the white grain at the bottom of each slice in Figure  2a-i. To ensure that the solution is unique for each angle, the discretization must be performed on a convex surface.
In our dataset, it has been verified that grain area and grain volume are respectively proportional to the square and cube of the 2D and 3D Feret diameters. As a consequence, these two parameters are relevant to describe the geometry of equiaxed dendrites. They are also quite easy to handle, to compute elongation factors and also to define orientation angles. Feret diameters (2D or 3D) alone are sufficient to precisely describe a 3D object because they cannot describe if an object is isotropic, and other size parameters need to be introduced in other directions of the space. Secondary and tertiary Feret diameters are defined similarly to 3D Feret diameter (also called 3D principal Ferret diameter): the secondary Feret diameter is defined as the longest distance found in a direction perpendicular to the principal Feret diameter, whereas the tertiary Feret diameter is the longest distance perpendicular to the principal Feret diameter and the secondary Feret diameters.
2D Identification of 3D Grains As said in Section 1, common metallographic measurements are two dimensional. With our dataset, it is possible to retrieve this kind of measurement using only the 2D contours. If each slice were considered individually, the average 2D Feret diameters, < F 2D >, would vary through the slices, as shown in Figure  5. The represented error bars are 50 % of the standard deviation (0. 5 • σ). The mean value, of all the grains on all slices, is 0. 67 cm with a standard deviation of 0. 15 cm. This mean value is not consistent with the variation observed through all the slices. On the first slice, the mean 2D Feret is 0. 45 cm, whereas on the last seven slices, the mean 2D Feret length is close to 0. 85 cm ± 0. 1 cm. In other words, it means that grain size measurements can give results varying up to a factor of two just by changing the sampling position for the 2D measurement by 1 cm, i.
The mean elongation factors are equivalent to a 5:3:2 ellipsoid. The most symmetrical case that exists is a 5:5:4 ellipsoid, and the most elongated one is equivalent to a 10:5:2 ellipsoid. There is no isotropic case in the analyzed volume. In comparison, the 2D elongation mean case is a 5:3 ellipse. The 2D value is close in comparison to the 3D one, but does not give any information regarding the 3D shape. The orientation of dendritic grains is also of interest. The orientation of the principal Feret diameter is represented in Figure  9. To obtain a density plot, kernel density estimation was applied to the data with the use of the seaborn python library,  [26]. This library is very useful because it allows one to directly see the density function for each variable along with the two-dimensional estimated density function.
Funding: This work was supported by  ASCO Industries and ANRTFrance  under a  CIFREPh. D. fellowship  (Grant Number  2015-1286 ). This work was supported by the  French State  through the program "Investment in the future" operated by the  National Research Agency (ANR)  and referenced by  ANR-11-LABX-0008-01  ( LabEx DAMAS ).
DNA repair proteins in 11 Deinococcus species: overview To get more insight in the DNA repair repertoire in the genus Deinococcus, the DNA repair genes in 10 other radiation-resistant Deinococcus species were searched and compared with that of the well-studied D. radiodurans. The conservation of novel proteins possibly involved in DNA repair is described in the sections 'The Ddr and Ppr proteins' and 'Miscellaneous proteins involved in resistance to radiation and other DNA-damaging agents in Deinococcus'. Most genes for important DNA repair mechanisms are highly conserved, whereas homologues of several DNA repair genes, such as recC and sbcB and genes for the NHEJ proteins LigD and Ku, are absent in each analysed Deinococcus species. Interestingly, we also observed many differences regarding protein presence/absence, domain composition or numbers of protein variants (see Table  2  for the main differences among the 11 Deinococcus species, and Table  S2  (Supporting Information) for accession numbers of all DNA repair proteins).
Recombinational DNA repair The proteins involved in recombinational DNA repair in D. radiodurans, such as Rec, Ruv and SSB proteins, are highly conserved in the other Deinococcus species. Nevertheless, there is some interesting diversity among the bacteria. Concerning the genetic   S2 , Supporting Information) made with Clustal omega. GenBank accession numbers in parentheses follow the species name. The phylogenetic tree was developed using the neighbour-joining algorithm in MEGA 6. 0. The scale indicates the number of amino acid substitutions per site, and the node numbers are bootstrap values based on 1000 replications. organisation of the recA gene, each Deinococcus species contains a cinA-ligT-recA gene cluster (probably operon in each), except D. proteolyticus that misses cinA and has a ligT-recA operon (cinA codes for competence/damage-inducible protein A; ligT encodes LigT-like RNA 2 ,3 -cyclic phosphodiesterase, originally identified as 2 -5 RNA ligase).
The main limitation of the existing methods lies in the difficulty to apply them: the code is usually not publicly available, and when available, the documentation is limited. Here we present a fully automated pipeline for the analysis of PET data on the cortical surface, both in the subject's space and in a common template. This pipeline enables the reproducible surface-based analysis of PET data on large datasets through a non-trivial combination of different tools, mainly from FreeSurfer. It is based on the Brain Imaging Data Structure (BIDS)  (Gorgolewski et al. , 2016)  2 for the organization of the input files, which is a standard adopted throughout the neuroimaging community. This pipeline is available within Clinica 3  (Routier et al. , 2018a) , an open-source software platform for clinical neuroimaging research studies.
Implementation The proposed pet-surface pipeline is part of Clinica, a software that aims at making clinical neuroscience easier and more reproducible  (Routier et al. , 2018a). Clinica contains three main parts. (  1 ) Feature extraction pipelines for different neuroimaging modalities (currently T1-weighted MRI, diffusion MRI, and PET). The pipelines are written in Python, based on the Nipype library  (Gorgolewski et al. , 2011) , and combine different software packages such as FreeSurfer, SPM, FSL, or PETPVC. (2) Statistics and basic machine learning tools that take the different types of features as input. (3) Tools for dataset management as well as tools to curate publicly available datasets such as ADNI and convert them into the BIDS standard  (Samper-González et al. , 2017 , 2018). Clinica uses the BIDS standard for inputs and the outputs of the different pipelines are stored under a specific BIDS-inspired structure called CAPS (ClinicA Processed Structure).
MATERIALS Datasets Part of the data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative database  5  The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial MRI, PET, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early AD. Over 1,650 participants were recruited across North America during the first three phases of the study (ADNI1, ADNI GO, and ADNI2). Around 400 participants were diagnosed with AD, 900 with MCI and 350 were control subjects. Three main criteria were used to classify the subjects  (Petersen et al.
Participants The first two sets of participants considered in our experiments were extracted from the ADNI dataset. The first dataset, denoted ADNI ALL , comprises 242 subjects who were either diagnosed as CN at baseline with negative amyloid status, or as AD at baseline with positive amyloid status, and for whom a T1w-MR and an FDG PET scan, with a known effective resolution (to estimate the point spread function), were available. The amyloid status of the subjects was determined from a PiB or an AV45 PET scan with thresholds of 1. 47 and 1. 10, respectively  (Landau et al. , 2013). The second dataset, ADNI SUBSET , is a subset of ADNI ALL that comprises 30 CN and 30 AD subjects. Tables  1, 2  summarize the demographics, MMSE and amyloid status of the ADNI ALL and ADNI SUBSET participants, respectively.
Forty-One patients with semantic variant PPA (svPPA), Twenty-Six patients with logopenic variant PPA (lvPPA), and Twenty-Two control subjects were extracted from the CAPP database (Table  3 ). In the following, CAPP SEMANTIC denotes the dataset composed of the svPPA and control subjects, and CAPP LOGOPENIC denotes the dataset composed of the lvPPA and control subjects. More details regarding this cohort can be found in  Routier et al. (2018b).
ADNI The acquisition protocols of the 3D T1-w MR images from ADNI 2 can be found in  Jack et al. (2010). When available the images corrected for gradwarp and B1-inhomogeneity were used, otherwise the original images were selected. The ADNI FDG PET protocol within ADNI GO/2 consisted of a dynamic acquisition of four 5-min frames, 30 to 60 min after injection  (Jagust et al. , 2015). Images at different stages of preprocessing (frame averaging, spatial alignment, interpolation to a standard voxel size, and smoothing to a common resolution of 8 mm full width at half maximum) are available for download. The images co-registered and averaged across time frames were selected. The curation of the ADNI database and its conversion to the BIDS format was performed using the adni-to-bids converter available within Clinica  (Samper-González et al.