dataesr/scientific-paragraphs-categorization huggingface dataset:
Mapping of the SLF We used diffusion tractography to identify the three branches of the SLF in 129 healthy right-handed volunteers (59 males and 70 females) aged between 18 and 79 years. For each participant, 60 contiguous near-axial slices were acquired on a 3 T GE Signa HDx TwinSpeed system (General Electric, Milwaukee, WI, USA) with the following parameters: rostro-caudal phase encoding, voxel size 2. 4×2. 4×2. 4 mm, matrix 128×128, slices 60, NEX 1, TE 93. 4 ms, b-value 3000 s/mm 2 , 60 diffusion-weighted directions and 7 non-diffusion-weighted volumes, using a spin-echo EPI sequence. Cardiac gating was applied with effective TR of 20/30 R-R intervals. Quality control of the data was assured using an automated analysis system  (Simmons et al. , 1999). Standard diffusion tensor tractography does not allow the reconstruction of the two most dorsal branches of the SLF because of the crossing of the dorsal association fibres with commissural and projection fibres (Thiebaut de  Schotten et al.
, 2011a Schotten et al. , , 2011b;; Catani et al. , 2012; Chechlacz et al. , 2015; Marshall et al. , 2015; Budisavljevic et al. , 2016; Cazzoli and Chechlacz, 2016). A modified (damped) version of the Richardson-Lucy algorithm for spherical deconvolution  (Dell'acqua et al. , 2010)  was employed using the software StarTrack (http://www. natbrainlab. co. uk). Algorithm parameters were chosen as previously described by our group  (Dell'Acqua et al. , 2013). Whole brain tractography selected every brain voxel with at least one fibre orientation as a seed voxel. From these voxels, we reconstructed the streamlines by sequentially piecing together discrete and shortly spaced estimates of fibre orientation to form continuous trajectories  (Dell'Acqua et al. , 2013). When entering a region with crossing white matter bundles, the algorithm followed the orientation vector of least curvature  (Schmahmann et al.
, 2007). Streamlines were halted when a voxel without fibre orientation was reached or when the curvature between two steps exceeded a threshold of 45°. The software estimating and reconstructing the orientation vectors and the trajectories from diffusion MRI was written in Matlab 7. 8 (http://www. matwork. com). Tractography dissections of the SLF I, II and III were performed using a multiple regions of interest (ROIs) approach: in each hemisphere three ROIs were delineated around the white matter of the superior, middle and inferior/precentral frontal gyri, and a ROI around the white matter of the parietal lobe. In order to exclude fibres belonging to the long and posterior segment of the arcuate fasciculus, which respectively connect frontal or parietal regions with temporal regions, a no-part ROI was delineated around the temporal white matter.
Further details can be found in (Thiebaut de  Schotten et al. , 2011a). For each participant, a convergence speed (CS) map of the deconvolution algorithm  (Dell'acqua et al. , 2006)  was estimated. CS map quantifies how quickly the residual fitting error between the diffusion signal, and the fibre model as identified by the deconvolution algorithm decays within each voxel. CS maps better contrast white matter regions showing a smaller partial volume effect, as compared to FA or similar anisotropy maps. CS maps were registered to the MNI152 template provided with the FMRIB Software Library package (FSL, http://www. fmrib. ox. ac. uk/fsl/) using Advance Normalisation Tools (ANTs, http://www. picsl. upenn. edu/ANTS/), which combines affine with diffeomorphic deformations  (Avants et al. , 2007; Klein et al.
Frontal and parietal peak coordinates from contrasts measuring the main or task-set effect of each function were collected, and a separate voxel-based meta-analysis was carried out for each of the 14 main functions using the Effect-Size Signed Differential Mapping software (www. sdmproject. com) (Radua and Mataix-Cols, 2012; Radua et al. , 2012). First, a standard Talairach map of the effectsize of the regional activation was recreated separately for each study by means of a Gaussian kernel, which assigns higher effect-sizes to the voxels closer to peaks (with the effect size of the peaks being derived from the corresponding t-values). Second, the mean maps were calculated using standard meta-analytical random-effect models, which account for the variance and sample size of each study as well as for the between-study heterogeneity.
Separate functional networks In order to investigate the pattern of segregation of the 14 investigated functions, we first calculated cross correlations among the meta-analytic maps, using the function fslcc provided in the FSL software package, which were preliminary to the following principal component analysis (Fig. 3 ). An 'activation' matrix was derived from the meta-analytic maps  (Johansen-Berg et al. , 2004). This matrix consisted of columns that indicated each meta-analytic map, and rows that represented the level of activation for each voxel in the frontal or parietal lobe. The correlation between the level of activation in each voxel for a certain function was correlated with the level of activation in the corresponding voxel for each of the other functions. Further, the 'activation' matrix was entered into a first principal component analysis in SPSS (SPSS, Chicago, IL) using a covariance matrix and quartimax rotation (with a maximum of 50 iterations for convergence), in order to estimate the number of principal components to extract for each function (Fig.
, 2013) , functional  (Koechlin et al. , 2003; Badre and D'Esposito, 2007, 2009) , and anatomical studies (Thiebaut de  Schotten et al. , 2016) , and provides further insight into the functional organisation of the frontal-parietal networks. Finally, although the current study benefitted from a very powerful meta-analytical approach, there are also some limitations that need to be acknowledged. Our combined approach offered the advantage of exploring several functions at the same time, and of being independent from an a priori hypothesis regarding tract functions. However, the meta-analytic maps were normalised and compared with the tractography reconstruction of white matter tracts derived from a different dataset. Hence, functional activation of cortical regions and structural information on white matter anatomy were based on two distinct populations, which limited our ability to take into account interindividual variability of the real anatomy.
Acknowledgements This work was supported by  Shire  (project  IST-ALB-000217 ), the  Agence Nationale de la Recherche  (project  PHENOTYPES , no. ANR-13-JSV4-0001-01 ), the  Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and Institute of Psychiatry, King's College London ; and the  Sackler Institute for Translational Neurodevelopment. In addition, we also thank the program " Investissements d'avenir"  ( ANR-10-IAIHU-06 ) for their generous support. Marco Catani  is recipient of a  Wellcome Trust   Investigator Award  ( 103759/Z/14/Z ). We would like to thank the NATBRAINLAB (http://www. natbrainlab. co. uk) for helpful discussion.
Grb14 expression is also increased in skeletal muscle of obese and insulin-resistant patients and decreased by weight loss, along with increased insulin sensitivity  20. All these data suggest that this protein could be involved in the decrease in insulin signaling observed in metabolic diseases. Targeting IR-Grb14 interaction, in order to relieve the Grb14 inhibitory action on the IR, may thus represent a new strategy to improve insulin signaling and action. To identify small non-peptidic inhibitors of the IR-Grb14 interaction, we performed structure-based virtual ligand screening experiments, based on the Grb14(BPS)-IR crystallographic structure  15, [21] [22] [23] [24]. After a structural analysis of docked molecules present in the ChemBridge database in two different pockets at the surface of the IR, we selected a list of 1000 molecules that were experimentally tested for disrupting the Grb14-IR interaction in an a-cellular BRET (Bioluminescence resonance Energy Transfer) assay  25, 26.
The ChemBridge database (containing 4,000,000 molecules) was scanned for compounds with shape similar to C1, C5, or C8, or compounds similar to both C1 and C5. Using structured-based and ligand-based virtual screening and different virtual filters, 51 molecules with high-score for potential inhibition of the IR-Grb14 interaction were selected, purchased and experimentally evaluated for inhibition of IR-Grb14 interaction using the a-cellular BRET assay (Fig. 2A ). This group contained all compounds similar to C1, C5 and C8, and 10 compounds similar to both C1 and C5 exhibiting the best properties. Some compounds displayed artifactual increase in BRET signal, due to direct effects on luciferase activity (C1-04, C1-14. C1/5-06, C1/5-07, C1/5-09, C1/5-10). Three new molecules (C1-13 and C1-26 similar to C1, and C5-03 similar to C5) were reproducibly detected as having inhibitory activity on BRET between IR and Grb14 (Fig.
4A  right panel). The effect of C8 on IR activation was further investigated in Western blotting experiments. In vitro insulin-induced autophosphorylation of partially purified IR was not altered by the addition of C8 (Fig. 4B ). Similarly, the autophosphorylation of endogenous IR in intact cells was not modified by the addition of C8 (Fig. 4C ). Altogether, these data show that C8 did not affect insulin-induced conformational changes within the receptor and did not modify IR autophosphorylation. Therefore, potential effects of C8 on IR signaling are unlikely to be due to direct activation of the receptor.
Methods All methods were carried out in accordance with relevant guidelines and regulations. Virtual docking and scoring. The crystallographic structure of the activated IRTK in complex with the Grb14 BPS domain  15  (PDB ID: 2AUH, resolution 3. 2 Å) was used for in silico screening. A multi-step docking/scoring protocol employing MS-DOCK  45  and Surflex  46  was used. Two binding pockets could be identified at the surface of the IR and the compound collection was docked on these two different pockets. The ChemBridge database (the starting collection contained about 500 000 unique molecules) was filtered with our software FAF-Drugs 47 and an in-house "soft" drug-like physicochemical property filter (http://fafdrugs4. mti. univ-paris-diderot. fr/filters. html) resulting in a final collection of 340,000 molecules.
Acknowledgements This work was supported by the  Agence Nationale pour la Recherche  ( ANR Physio-Grb14 ), by the  French Foundation for the Medical Research (FRM , labélisation équipe) and by a grant "proof of concept" from the  Institut Cochin. The work was performed within the  Département Hospitalo-Universitaire (DHU) AUToimmune and HORmonal diseasesS. Anaïs Gondoin  holds a PhD grant from the  French Ministry of Research. We acknowledge  Solenne Marmier ,  Régine Cabrae  and  Michèle Caüzac  for preparation of cultured hepatocytes,  Solenne Marmier  for mouse gavages, and  Virginie André  for initial experiments in the synthesis of C8.
, 1994). Those expressing each Tcrbj were as described previously  (Candeias et al. , 1991). As expected in polyclonal repertoires, CDR3 lengths followed a Gaussian distribution, from 7-16 amino acids (aa) (Fig. 2C ). This data indicates the efficiency of our methodology: we amplified all Tcrbv and Tcrbj at the expected frequencies, and our sequencing efficiency was high. To determine if PCR errors could influence our results, in each experiment we also sequenced the Tcrb expressed by multiple single-cells from Monoclonal (Mo) P14 TCR-Tg mice. All the 184 MoP14 Tg cells we studied in different experiments expressed the same Tg Tcrb, excluding the possibility that PCR errors influenced our results (Supplemental Table  2 ). The present data also allowed us to determine other characteristics of the Tcrb rearrangements.
Estimating the mean clonal size of the PB1-F262 subset Quinn et al. studied the number and phenotype of influenza A virus-specific CD8 + T cells, selected using tetramers, in B6 mice  (Quinn et al. , 2016). They performed single-cell Tcra and Tcrb sequencing of one such population, specific for D b PB1-F262 influenza A virus peptide. Consistent with our results, they observed that the naïve repertoire was almost completely diverse, with only a small number of repeated Tcrb sequences. Here, we take their published Tcrb chain sequencing data (Dataset S1) and obtain estimates of the mean number of cells per clonotype, using [2] with S=150. • Mouse 1: 30 sequences, 28 unique, estimate ń=1. 36. • Mouse 2: 28 sequences, 28 unique, estimate ń=1. 00. • Mouse 3: 11 sequences, 11 unique, estimate ń=1.
It must be noted that the majority of the repeated sequences found in CD8 + T cell repertoires express the selected Tcrbv, i. e. , those Tcrbv that are more abundant in in-frame than out-of-frame rearrangements. We conclude that by using different mathematical approaches, and by studying different samples collected from up to 13 mice as well as the data from  (Quinn et al. , 2016)  collected from 10 mice, the average clonotype size of peripheral CD8 + T lymphocytes is 1. 1 cell. These results show that taking into consideration only Tcrbv diversity, the number of unique TCRB chains approaches the number of naïve T cells.
Acknowledgments: The  European Union Seventh Framework Programme  ( FP7/2007(FP7/ -2013 ) ) under grant agreement  317040  ( ITN QuanTI ) funded this study, and the researchers P. Gonçalves and  M. Ferrarini. We thank  António Freitas  for their critical reading of the manuscript and comments. Appendix A. Supplementary data: Supplementary data associated with this article can be found, in the online version, at….
Microsatellite analysis Four microsatellite (MS) markers (MS3. 502, MS3. 27, MS6, and MS8) were amplified using semi-nested PCR amplification as previously described  [21, 22]. The length variation of labelled PCR products was measured in comparison to internal size standards (GeneScan 500 LIZ; Applied Biosystems) on an ABI Genetic Analyzer (Macogen Inc. , South Korea). Allele lengths and peak heights were analysed by using PeakScanner software (Applied Biosystems). Multiple alleles were called when there were multiple peaks per locus and where the height of these minor peaks reached one-third or more (>33%) of the predominant allele peak height.
Plasma cytokine quantification The plasma pro-and anti-inflammatory cytokines were quantified by human cytometric bead array (CBA) kits (BD Biosciences Pharmingen, San Jose, CA, USA) for IL-1β (interleukin-1 beta), IL-6, IL-8, IL-10, IL-12p70 and TNF (tumour necrosis factor), and by CBA Flex Set system (BD Biosciences Pharmingen, San Jose, CA, USA) for IL-2, IL-4, IL-5, IL-13, IL-17A and IFN-γ (interferon gamma). Standard curves for each cytokine were generated by using the reference cytokine concentrations supplied with the kits. All standards and plasma samples were acquired on Accuri ™ C6 Plus personal flow cytometer (BD Biosciences). Data analysis was performed using the FCAP Array software version 3. 0. 1 (BD/Softflow, Pécs, Hungary) and the values were expressed in pg/mL.
Recently, two manuscripts were published which created an algorithm to identify statin-associated muscle symptoms from claims databases and/or electronic medical records  [16, 17]. Furthermore, a patient-level tool, which allows patients to start a dialog about statin-associated muscle symptoms with healthcare providers, has also been developed  [18] ; and finally, the American College of Cardiology has developed a mobile application to identify statin-associated muscle symptoms. In our study, the SAMS-CI was developed to assist clinicians' estimates of the likelihood that myalgia or myopathy are attributable to statins. The current study represents the first important step on the road to final validation. A next step in this process will be to collect evidence about the performance of the SAMS-CI with actual statin patients in practice.
Consequently, as a first step toward validation, we are carrying out a post hoc reanalysis of data from these two trials. Although the SAMS-CI did not exist at the time of these studies, enough detailed data was collected on each patient at baseline to complete the SAMS-CI retrospectively. These scores will then be compared to the patients' ultimate outcomes in the studies, to estimate the SAMS-CI' ability to predict statinassociated muscle symptoms among patients on statins as well as to identify patients who experienced muscle symptoms on placebo. These results may suggest further revision of the instrument before a dedicated, prospective study is carried out.
Acknowledgements The authors thank the physicians who provided feedback on optimization and clinical use of the  SAMS-CI , and acknowledge the contributions of  Rita Samuel, MD ,  William Sasiela, PhD  ( Regeneron Pharmaceuticals, Inc. ) and  Gloria Garcia-Palacios, PharmD (Sanofi)  for providing critical review of the manuscript. Minor editorial and administrative support for later drafts was provided by  Rachel Wright, PhD , of  Prime (Knutsford, Cheshire, UK ), funded by  Sanofi and Regeneron Pharmaceuticals, Inc.
Methods The presented survey was conducted in August 2014 by the professional survey company SSI in seven European countries. Participation was voluntary. Data was evaluated anonymously. The online study had a total of 2454 participants, with 1809 respondents remaining after quality control (German sample n = 274, French sample n = 243, Spanish sample n = 241, Portuguese sample n = 290, Romanian sample n = 276, Serbian sample n = 291, and UK sample n = 194). The age distribution was balanced with a mean age of approx. 40 years, with 49. 1% male and 50. 9% female. The mean education of the respondents was 15. 2 years. Regarding employment, most of the respondents were in paid work (57%, including employees, self-employed, and working for the family business), 11. 3% of the respondents were unemployed and actively looking for a job, and 9.
Mobile phones and base stations use EMF for transmission of voice and data. " All questions were translated into languages of the participating countries (German, French, Spanish, Portuguese, Dutch, Romanian, Montenegrin, Serbian) and double-checked with re-translation back into English. Demographic, political, and economic background-related items were partially adapted from the survey platform "European Social Survey"  [24]. The dataset used and analyzed during the current study is available from the corresponding author on reasonable request. For a detailed analysis, three groups were formed according to the subjects' responses regarding the components of RF EMF risk perception, i. e. , concern, thematic relevance, and discursive relevance (see Table  1 ). The grouping of the study subjects into one of the three groups is based on the following assignment: (1) Not and less concerned group: subjects who scored ≤3 on the 5-point Likert scale for concerns, as well as for thematic and discursive relevance (n = 931); (2) not enduringly concerned: subjects that responded with 4 = "fairly concerned" and 5 = "very concerned" regarding concerns about the potential health effects of electromagnetic fields, but ≤3 on questions regarding relevance (n = 548); (3) enduringly concerned: subjects who scored ≥4 for concerns and 4 = "often" or 5 = "very often" on thematic relevance (n = 228).
(Question: "Roughly at what distance (m) would you accept a base station close to your home?". ". if the exposure was reduced by 30%?", ". if the exposure was reduced by 50%?", ". if the exposure was reduced by 70%?"). Further details are provided elsewhere  [21]. All analytical calculations were conducted using IBM, SPSS ® , V20 (IBM, Armonk, NY, USA).
Participants Thirteen healthy volunteers (age 44. 8 ± 14. 5, 7 male) participated. All were right-handed (Edinburg Handedness Inventory,  Oldfield 1971 ). The study protocol was registered as clinical trial (NCT01554449) and approved by the local ethics committee. The procedures complied with the ethical standards outlined by the Declaration of Helsinki. All participants gave written consent before inclusion. The study is part of a larger protocol focussing on rehabilitation of the upper limb early post-stroke (MARGAUT: 2010-A00596-33). These volunteers are included in the control group of the MARGAUT study. Exclusion criteria were as follows: (1) age >18 years, (2) history of neurological, psychiatric or orthopaedic disease, (3) medical treatment impacting the nervous system, (4) substance use, and (5) contra-indications to MRI.
Kinematic data analysis First, the shape of the movement was defined by calculating the frequency (FREQ: how fast) and the amplitude (AMP: how large) of the principal oscillation. We calculated the absolute distance between the lowest and highest point of each sub-cycle and defined the mean of all sub-cycles to get AMP. The total number of cycles within time was then used to calculate the FREQ of the principal oscillation. Second, three dimensionless variables were calculated to define the structure of the movement: the smoothness of the movement, the directness of the trajectory in space, and the irregularity of the movement's structure orthogonal to the principal oscillator. These variables provided more detailed information on the control strategy used. The smoothness of the movement was quantified by counting the number of velocity peaks (NVP) on the x, y, z time series.
This rationale guided the applied data analysis: the way we calculated SampEn was not based on the primary voluntary oscillation, but the captured irregularity of the variation along the main oscillation, i. e. the corrections of the continuous falls out of the goal trajectory. To sum up, the NVP captures the amount of submovements during the oscillation; the nTL captures the amplitude of the directional variability of the hand-path trajectory in space; And the SampEn captures the irregularity of this variability in the plane orthogonal to the principal oscillator independent of its amplitude; i. e. the automaticity of control. The kinematic variables are mathematically independent and are normally distributed. Differences between left, right, and bimanual movements for each kinematic variable were evaluated with repeated measures ANOVA.
fMRI data analysis Spatial preprocessing was performed using SPM8 (http:// www. fil. ion. ucl. ac. uk/spm/) and MATLAB (R2012a, The MathWorks). Images were re-oriented to the anterior commissure, slice-time corrected, realigned to the first volume, co-registered, normalised to T1 template (provided by Montreal Neurological Institute MNI), and smoothed (8 mm FWHM Gaussian filter). For each experimental condition, a general linear model analysis was performed based on the time-course model of the BOLD-response function in relation to the stimuli onset, to define brain activations related to the motor task. Realignment parameters were included in the model. Statistical t-maps comparing the active blocks with resting blocks were generated on a voxel-by-voxel basis. Contrast between 'active' and 'rest' was then calculated per participant per condition.
The identified resilience network can be utilized to optimize medical, police, and emergency services as well as suggest policy changes enhance public safety and health  [12]. The experimental data is comprised of the number of police-reported incidents, organized by city, throughout MC between 01/01/2014 and 06/26/2016, 1  constituting a spatiotemporal multivariate time series with each data stream reporting one variable -the number of daily city-wide crime incidents. The resilience network capitalizes on underlying temporal trends within a city and shared temporal trends between cities to improve crime prediction, thus mitigating the effects of data loss and day-to-day crime rate variability. An investigation was also conducted to discover potential underlying demographic and topology parameters that may explain the evaluated network graph.