dataesr/scientific-paragraphs-categorization huggingface dataset:
N. Arav  and  G. Kriss  gratefully acknowledge support from  NASA/XMM-Newton Guest Investigator  grant  NNX09AR01G. Support for HST Program numbers 12022 and 12916 was provided by  NASA  through grants from the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS5-26555. E. Behar  was supported by a grant from the  ISF. S. Bianchi ,  M. Cappi , and  G. Ponti  acknowledge financial support from contract  ASI-INAF n. I/088/06/0. P. -O. Petrucci  acknowledges financial support from  CNES  and from the  Franco-Italian PICS  of the CNRS. G. Ponti acknowledges support via  EU Marie Curie Intra-European Fellowships  under contract no. FP7-PEOPLE-2009-IEF-331095  and  FP-PEOPLE-2012-IEF-331095.
Neuroanatomical connectivity matrix Weighted neuroanatomical connectivity matrices were extracted from the diffusion tensor imaging (DTI) data of 21 healthy, normal participants (11 males, aged 22-45 years). Extraction methods were based on  Gong et al. (2009). Diffusion MRI was acquired by using a singleshot echo planar imaging-based sequence with coverage of the whole brain; repetition time (TR), 9390 ms; echo time (TE), 65 ms. DTI images utilized 32 optimal nonlinear diffusion weighting directions (b = 1200 s/mm2) and 2 non-diffusion weighted volumes; reconstructed matrix = 128x128x45; reconstructed voxel size 2. 0 mm × 2. 0 mm × 2. 0 mm. We also acquired T1-weighted structural images with a threedimensional 'FLASH' sequence (TR = 12 ms, TE = 5. 6 ms, flip angle = 19°, with elliptical sampling of k-space, giving a voxel size of 1 × 1 × 1 mm in 5.
Data collection MEG data were recorded from ten healthy participants who underwent a five minutes resting-state scan with their eyes closed. Recordings were performed at 1000 Hz sampling frequency on an Elekta Neuromag (Elekta Neuromag Oy, Helsinki, Finland) with 102 magnetometers and 102 pairs of orthogonal radial gradiometers. Subjects' head shape was recorded using a Polhemus Isotrack system, and four head position indicator (HPI) coils allowed for the head to be localized in the scanner.
Data analysis Data preprocessing included signal space separation  (Taulu et al. , 2005) , de-noising with independent component analysis, source reconstruction and bandpass filtering of the MEG signal. Signal space separation compensates for external interference and sensor artifacts by projecting the MEG data onto a basis set of spherical harmonics. Harmonics corresponding to sources originating from within the sensor array are preserved while interfering sources from outside the environment surrounding the sensor array are rejected. The sensor space MEG data were de-noised using temporal independent component analysis (ICA) to remove cardiac, 50 Hz mains and, in some subjects, eye movement artifacts. Each dataset was then co-registered into the MNI space by registering the canonical MNI template to the Polhemus head shape data.
A local sphere forward model  (Huang et al. , 1999)  was then estimated using the subject's head shape. Both co-registration and forward model estimation were performed with the Matlab SPM8 package (FIL,UCL). The MEG data were then bandpass filtered into delta (1-5 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), low gamma (30-48Hz) and high gamma (52-80 Hz) bands. An LCMV beamformer was used to transform the original sensor time series for each frequency band into source space time series, that is, to reconstruct the activity at the 90 locations defined by the AAL brain parcellation. Functional connectivity scores between node pairs were estimated by taking the band-limited power envelopes of a pair of nodes, regressing one out from the other to orthogonalize the time series and thereby removing any zero-lag correlations.
W, and the propagation velocity v are the main parameters to be varied in the simulations to find an area of parameter space where the correspondence between empirical and model data becomes maximal. In the brain, delays depend on axonal transmission times, which are determined by axon diameter, myelinization, and distance. The delay between two brain areas in the model was determined by their Euclidean distance in MNI-templated source space (max = 160 mm) divided by v, with propagation speed v in m/s and inter-area distance D in mm. The Euclidean distances are necessarily a lower bound estimate of fiber tract lengths, therefore propagation speeds would here be somewhat underestimated.
4 for any delay condition, which leaves room for improvement. Of course, this may be related to dynamics and communications not captured by the model, such as, e. g. , lateral connections  (Spiegler and Jirsa, 2013) , or directionality of fibers between brain areas. Results are also influenced by the quality of the DTI matrix, which is prone to miss interhemispheric connections  (Hagmann et al. , 2008). There is ongoing work in our lab to enhance the quality of the DTI by integrating FC and SC through a modeling approach. The DTI acquisition parameters from the used dataset at 1. 5 T and b = 1200 may also have limited the SC precision. Another aspect to consider is the choice of brain parcellation and preprocessing, which may influence connectivity estimates  (Cloutman and Lambon Ralph, 2012; Van Essen et al.
Acknowledgments TTN was supported by the  SUR of the DEC of the Catalan Government  and by the  FSE. GD was supported by the  ERC  Advanced Grant: DYSTRUCTURE (n. 295129), by the  Spanish Research  Project  SAF2010-16085  and by the  CONSOLIDER-INGENIO 2010 Programme   CSD2007-00012 , and the  FP7-ICT BrainScales. The research reported herein was supported by the  Brain Network Recovery Group  through the  James S. McDonnell Foundation. MJ was supported by the  MINDLab Investment Capital for University Research Fund. MLK was supported by the  TrygFonden Charitable Foundation. We thank  Sven L. Hilbert  and  Katharina Glomb  for useful discussions on earlier versions of this manuscript and two anonymous reviewers for their helpful comments and suggestions.
Background: In late 2007, some Swedish County Councils started 7-valent pneumococcal conjugate vaccine (PCV7) implementation for children, and PCV7 was included in the national immunization program in 2009. By 2010, both PCV10 and PCV13 were licensed, and the selection of vaccine was subject to County Councils tenders. This study investigated the impact of the order of PCV introduction into vaccination programs on the incidence of all-cause pneumonia hospitalizations in children ,2 years-old. Methods: Using population-based data from the publicly available National Inpatient Registry, the incidence of inpatient pneumonia (ICD-10 J12-J18) hospitalizations by County Councils among children ,2 years old was identified between 1998 and 2012. Incidence rate ratios (IRR; 95% CI) were calculated during the nationwide implementation of PCV7 and then between County Councils, as based on the higher-valent vaccine chosen for a program.
Data sources The present study is a retrospective analysis of aggregated data from the publicly available National Inpatient Registry administrated by the National Board of Health and Welfare that includes nationwide information on hospital admissions and discharges since 1987 in Sweden  [22, 23]. The proportion of data not properly registered in the National Inpatient Registry has been estimated to be ,1%  [22]. Each inpatient discharge record includes date of hospital admission, the age and sex of the patient, and discharge diagnosis coded according to the International Classification of Diseases (ICD). In the analysis, hospitalization for all-cause pneumonia was defined in accordance with the codes in the ICD-10 (J12-J18): J12 (viral pneumonia), J13 (pneumonia due to Streptococcus pneumoniae), J14 (pneumonia due to Haemophilus influenzae), J15 (bacterial pneumonia), J16 (pneumonia due to other infectious organisms), J17 (pneumonia in diseases classified elsewhere), and J18 (pneumonia, unspecified organism) (Figure  1 ).
For the present study, all-cause pneumonia admission, irrespective of the causative agent, was used, as etiology is mostly unknown in children. The current standard for making the diagnosis of pneumonia is an X-ray-confirmed infiltrate in a child with acute respiratory disease, which would include cases with a viral cause also. We have used the hospital discharge diagnosis, and although the absolute majority of these are X-ray confirmed, there is no obligate requirement to have an X-ray-confirmed infiltrate to make the diagnosis, and thus a small minority may not be confirmed. In addition, the total number of hospital admissions for any cause of disease (i. e. , all ICD-10 codes together) was identified by calendar year and County Council. Demographic characteristics for each County Council were assembled using public information from the government agency, Statistics Sweden  [24] , while information on health care characteristics was retrieved from a public statistical database of the Swedish Association of Local Authorities and Regions  [25].
PCV7 was included in the Swedish NIP in January 2009 in a 2+ 1 infant schedule (vaccinated at 3, 5 and 12 months of age) with no catch-up, although the first County Council had already started vaccination with PCV7 in October 2007. PCV coverage for children in Sweden born in 2010 was 97. 6% (range 96. 4-98. 6% between County Councils)  [26]. Each County Council tenders PCVs independently or together with other County Councils, leading to different tender criteria and evaluation algorithms between County Councils. In 2010, the County Councils introduced either PCV10 or PCV13, although one cluster of County Councils implemented PCV7, changed to PCV13, and then changed again to PCV10. Four County Councils had PCV introduction dates that differed from all other County Councils; the populations of these County Councils are small, representing about 10% of the source population, and available data did not allow a separate analysis for each one of them.
Statistical methods Results are shown as mean AE standard error of the means (SEM). Statistical analyses were performed with GraphPad Prism 1 5 (GraphPad Software, Inc. , La Jolla, CA) and R packages (R Core Team, URL: http://www. R-project. org/). The Western blot, RT-qPCR and laser Doppler experiments were first analyzed by one-way analysis of variance (ANOVA) with a Dunnett post hoc test to compare the preserved groups to a reference group (either CTL or WI-60). Then, the values from the four preserved groups were analyzed by twoway ANOVA to isolate the machine effect, the solution effect and the potential interaction between the two factors (machine, solution). Comparison of the resistance index during MP was performed using a Mann-Whitney test. Statistical significance was accepted for p < 0.
Acknowledgments We deeply thank,  Irene Launay ,  Catherine Henry ,  Pierre Couturier  and  William He  ´brard for their excellent technical assistance,  Sylvain Le Pape  for his help with R-statistics and  Raphael Thuillier  for his critical read of the manuscript. This study was supported by: the  ''Region Poitou Charentes'' (France), ''CHU de Poitiers and INSERM'' (France)  and  N. Chatauret  is a recipient of a ''The  FEDER-Region Poitou-Charentes '' grant (# 34474 ).
18 The CHS recruited participants from 4 communities: Forsyth county, NC (36. 1°north, 80. 3°west); Sacramento county, CA (38. 5°north, 121. 4°west); Washington county, MD (39. 6°north, 77. 8°west); and Pittsburgh, PA (40. 4°n orth, 80. 0°west). The cohort consisted of 5,201 adults recruited in 1989-1990 and an additional 687 African-American participants recruited in 1992-1993. Of these 5,888 participants, 4,692 ambulatory participants had complete exam data in 1992-1993 (the baseline assessment for the current study). Serum 25-hydroxyvitamin D (25(OH)D) concentrations were not measured in 1,424 participants who had prevalent cardiovascular disease or stroke (one or more of the following: coronary heart disease, congestive heart failure, claudication, atrial fibrillation, pacemaker, implantable cardioverter defibrillator, stroke, or TIA), determined by medical records, ECG findings, and selfreport.
Secondly, the controller is able to deal with perturbations, which may blur the performance of the simulation. Nevertheless, we tune the control parameters once with the compliant contact models and keep them constant during all experiments. Table  1  gives an overview of the different simulation configurations that are evaluated. The type of contact models, the choice of the generalized coordinates, the prox schemes and the calculation methods of the bilateral impulses are compared with each other. The focus of the evaluation clearly lies on the computation effort, which is very important for robotics. Therefore, all algorithms are implemented in C++11 for comparison. The computations of the kinematic and dynamic quantities such as Jacobians or the mass matrix utilize pre-generated code. The equations are symbolically optimized with the Symbolic Toolbox of Matlab with our open-source toolbox proNEu  [23].
Phenetic analyses of the RAPD results The bands observed in the gels were scored as absent (0) or present  (1) ; the analysis included all bands regardless of their relative intensity. As RAPD products are considered as dominant markers, similarity indexes according to Nei and Li (NLi) or Jaccard (Ji) were estimated using the MVSP software package (Multi Variate Statistical Package 3. 1)  [48] , thus taking into account only the present bands. The distance matrix was used to establish the phenetic relationship and draw dendrograms using the unweighted pair-group method with arithmetic means (UPGMA), also using the MVSP package.
Bioinformatic analyses Plasmid sequences were manually removed from each insert sequence generated. These sequences were then submitted to BLAST analysis for homology searches on a local bioinformatics toolbox  [49]  constituted by updated versions of the different L. major, L. infantum, and L. donovani genomes downloaded from the Sanger site (http://www. sanger. ac. uk/) and a set of bioinformatics programs. We used the Artemis  [50] , ACT  [51]  and ClustalW  [52]  software for annotation, visualization of multilevel comparison of the sequences to the genomic hits, and multiple sequence alignment, respectively. Sequences were also compared to their corresponding hits on the genomes as regards point mutations or indels using a homemade script (Mutation checkup); presence of microsatellites was screened using a script (Misa cluster) developed based on Misa program  [53].
Simple sequence repeats were considered as microsatellites when the number of repeats was $10 for mononucleotide motives, $6 for dinucleotide motives, $4 for trinucleotide motives, and $3 for tetra and pentanucleotide motives  [54]. Imperfect microsatellites correspond to imperfect repeat sequences (eg: (T)6(TG)6C(TG)7*). The sequences of the markers were submitted to GeneBank under the accession numbers reported in Table  2.
donovani parasites from Ethiopia and Saudi Arabia on one hand, and Kenya and India on the other (Figure  S1 ). The highest similarity (NLi. 0. 9) was observed among the Sudanese strains and among the 3 East African L. donovani strains, and the lowest ones (0. 77,NLi,0. 8) were between the L. infantum and L. donovani strains or between some L. archibaldi and the Indian L. donovani strains (Table  S2 ). This clustering was conserved with the Jaccard's coefficient (data not shown). When we reduced the dataset to the profiles obtained with the 9 primers that amplified the 22 cloned RAPD bands (here reported), the dendrogram still highlighted a similar clustering of the parasites according to their geographical origin but the relationship of the clusters into taxa was different; similarity values were still high (Figure  2 ).
The profiles revealed by these 3 probes were also similar among themselves (Figure  3 ). This conclusion was also confirmed by positive hybridization of each of these probes to Southern transfers of kDNA minicircles amplified by PCR primers AJS83 & DBY  [55]  (data not shown). In case of the M106/950/OPAD17 marker, the genomic Southern blot analysis showed hybridization with L. donovani and L. infantum DNA but not with L. major as predicted by the homology and coverage percent (80% and 51%, respectively); it did react with L. tropica DNA (data not shown). This confirms the sequence as an L. donovani/L. infantum/L. tropica specific marker. In silico analysis highlighted features of the RAPD markers and confirmed their potential as polymorphic DNA markers Nucleotide sequence analysis of the 16 cloned nuclear RAPD markers, using Artemis and ACT software allowed their finer mapping on the respective chromosomes.
29. 1110 gene. This likely corresponds to an artefactual event that was possibly generated during the RAPD reaction, as described elsewhere  [70, 71]. Interestingly, nuclear and kinetoplastic markers were identified in a proportion (15. 79% of kDNA) that is within the range described for these genomes in Kinetoplastids (,5-20%)  [72]. All nuclear markers were precisely mapped on the databases while no homologous sequences to the kinetoplast markers were found. This indicates they likely belong to variable regions of minicircles that are so far not described. In addition all (but one) of the nuclear markers were found mapping on medium to large sized chromosomes. Similar observations were made in previous studies where the analyzed markers were also found located on large chromosomes  [24, 43].
Data sample and event selection Several samples are used in the construction and validation of the variables entering the quark/gluon discriminant: dijet events, trijet events, γ+jet events, γ+2-jet events, t t events and W +jet events. After basic data quality requirements are imposed to remove known detector errors and readout problems, the selected dataset corresponds to a total integrated luminosity of 4. 67±0. 08 fb -1  [23]. The data were collected from March to October 2011 at a centre-of-mass energy √ s = 7 TeV. The average number of additional pp collisions per bunch crossing, called "pile-up", rose during the data-taking period from a few to 15.
Monte Carlo simulation Simulated event samples are generated for comparison with data and for the determination of the systematic uncertainties based on variations in the MC generator settings. For the MC samples, several different generators are used. MadGraph  [24]  is run as a 2 → N generator with MLM matching  [25] , uses the CTEQ 6L1 parton distribution function (PDF) set, and is interfaced to Pythia 6 with a version of the ATLAS MC11 Underlying Event Tune 2B (AUET2B)  [26]  constructed for this PDF set. Herwig++  [27]  is run standalone as a 2 → 2 generator and uses the MRST LO** PDF set with the LHC-UE7-2 tune  [28]. This tune of Herwig++ has an improved description of colour reconnection in multiple parton interactions and has been shown to have fair agreement with ATLAS data in minimum-bias observables  [28].
Pythia 6 is also run standalone as a 2 → 2 generator with the MRST LO** PDF set and the AUET2B tune. The AUET2B tune incorporates ATLAS  [29]  and CDF  [30]  jet-shape measurements as well as ATLAS fragmentation function measurements at √ s = 7 TeV  [31]  and is thus expected to describe inclusive-jet properties well. Additional pile-up events, which are superimposed on the hard-scattering event, are generated with either Pythia 6  [32]  with the AUET2B tune using the MRST LO** PDF  [33]  set, or Pythia 8  [34]  with the 4C tune  [35]  using the CTEQ 6L1 PDF set  [36]. Choosing between these two pile-up simulations has negligible impact on the analysis. The number of pile-up events in the MC simulation is reweighted to match the conditions found in the data for each trigger selection. The events are passed through the ATLAS detector simulation  [37] , based on GEANT4  [38] , and are reconstructed using the same software as for the data.
The performance of the tagger is determined using the two-dimensional extracted distributions of n trk and track width in data and those obtained for labelled jets in MC simulations. Systematic uncertainties on the evaluated performance are estimated using alternative templates as described in Sec. 5. 3. Table  1  summarises this performance for jets with |η| < 0. 8. The efficiencies for gluon-jets and quark-jets are evaluated only at certain operating points with fixed light-quark efficiency. Statistical uncertainties are evaluated using pseudoexperiments. Systematic uncertainties are combined in quadrature and affect both the quark-and gluon-jet efficiency in data. Large differences between MC simulation and data in the variables used translate into large scale factors in the gluon-jet efficiency.
solani, has been recently identified largely in Europe and beyond (4). The D. solani strain RNS 08. 23. 3. 1A (PRI3337) was isolated from potatoes in France in 2008  (5). Here we report the de novo genome assembly of D. solani strain RNS 08. 23. 3. 1A using Illumina HiSeq 2000 v3 technology. Two libraries were constructed using the TruSeq SBS v3 sequencing kit, a shotgun (SG) paired-end library with a fragment size of 150 to 500 bp and a long-jumping-distance (LJD) mate-pair library with an insert size of 6,000 bp. The two libraries were sequenced using the 2ϫ100-bp paired-end read module by Eurofins Genomics (France). Assembly of the sequences was carried out by use of the software CLC Genomics Workbench (v5. 1) from CLC bio. Sequence reads with low-quality (limit 0. 05), ambiguous nucleotides (n Յ 2) and sequence lengths of Ͻ30 nucleotides were dropped for assembly.
95). Then, using homemade script and fastqselect. tcl from the MIRA3 package, the mapped reads for both orientations (R1 and R2) were retrieved and de novo assembled (read length, 0. 5; identity, 0. 8). The remaining gaps were resolved by Sanger sequencing of PCR amplicons. The assembled genome of D. solani strain RNS 08. 23. 3. 1A is composed of one circular chromosome, containing 4,923,734 bp. The GϩC content was 56. 11%. Gene prediction using the RAST (Rapid Annotation using Subsystem Technology) v4. 0 automated pipeline  (7)  revealed the presence of 4,337 open reading frames. Nucleotide sequence accession numbers. This whole-genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession number AMYI00000000. The version described in this paper is version AMYI01000000.
As an example, the b-tagging performance can significantly deteriorate in the presence of large misalignments  [5, 8]. Electroweak precision measurements are also sensitive to systematic misalignments. Likewise, the imperfect knowledge of the tracker geometry is one of the dominant sources of systematic uncertainty in the measurement of the weak mixing angle, sin 2 (θ eff )  [9]. The methodology of the tracker alignment in CMS builds on past experience, which was instrumental for the fast start-up of the tracking at the beginning of LHC operations. Following simulation studies  [10] , the alignment at the tracker integration facility  [11]  demonstrated the readiness of the alignment framework prior to the installation of the tracker in CMS by aligning a setup with approximately 15% of the silicon modules by using cosmic ray tracks.
Before the first proton-proton collisions at the LHC, cosmic ray muons were recorded by CMS in a dedicated run known as "cosmic run at four Tesla" (CRAFT)  [12]  with the magnetic field at the nominal value. The CRAFT data were used to align and calibrate the various subdetectors. The complete alignment of the tracker with the CRAFT data involved 3. 2 million cosmic ray tracks passing stringent quality requirements, as well as optical survey measurements carried out before the final installation of the tracker  [13]. The achieved statistical accuracy on the position of the modules with respect to the cosmic ray tracks was 3-4 µm and 3-14 µm in the central and forward regions, respectively. The performance of the tracking at CMS has been studied during the first period of proton-proton collisions at the LHC and proven to be very good already at the start of the operations  [14] [15] [16].