dataesr/scientific-paragraphs-categorization huggingface dataset:
Also, it is not yet possible for the treating physician to evaluate the data, and possible patient concerns (e. g. , data protection, central data collection, and commercialization) need to be discussed and resolved in the long term. The Allergy Diary app is currently available in 15 different languages and can be downloaded for free in the Google Play Store and the Apple App Store. Similar electronic VAS diaries have already been used in numerous randomized, controlled studies  [50, 51].
Assessing neural latency with cross-correlation In our spatiotemporal analysis, the earliest significant difference between spatial frequency activation was found at 160 ms, where field intensity was greater for LSF than HSF, followed by significantly greater activity for HSF than LSF at 170 ms onwards. To determine whether this apparent temporal difference was significant, we performed cross-correlation analyses. This approach assumes that two paired waveforms (i. e. , LSF and HSF ERFs at each sensor) are highly correlated, which was indeed the case in these dataset (average R 2 across channels and subjects ϭ 82. 79%). Thus, we computed the relative lag between these waveforms at each of the 204 sensors. This resulted in 96 (47. 06%) of sensors with a significant lag (Bonferronicorrected, p Ͻ 2.
DCM of neural networks DCM is a biophysically informed method of comparing the likelihood of hypothetical neural networks that underlie a given dataset, compromising between model accuracy and complexity  (David et al. , 2006). We applied this technique in two stages: an anatomical stage, where we compared the likelihood that cortical, medial, and subcortical connections were recruited across all conditions; and a functional stage, where we took the winning model from the previous stage and compared the likelihood that connections were modulated by spatial frequency and/or emotion. In the anatomical stage, four families of models were constructed (Fig. 4 ). Each family consisted of models for each possible combination of forward and backward connections, resulting in four models for the Cortical family, eight models for Dual, eight models for Medial, and 16 models for All.
04%), demonstrating that all pathways, except the subcortical pathway, were likely modulated by both spatial frequency and emotion within the given dataset. To infer the generalizability of this neural network to the population, classical one-sample t tests were conducted on individual parameter (i. e. , connection) estimates. After Bonferroni correction for multiple comparisons, only the modulation of the cortical connection from V1 to the amygdala was significantly Ͼ0 (Bonferroni-corrected p ϭ 0. 019). The modulation of this cortical connection was greater for HSF than LSF (F (1,25) ϭ 11. 312, p ϭ 0. 002) and for fearful than neutral HSF faces (F (1,25) ϭ 6. 403, p ϭ 0. 018), suggesting a role for V1 in processing facial expressions in the HSF domain. Finally, we calculated the difference in functional modulation of the cortical route for spatial frequency (HSF-LSF) and emotion (fearful-neutral), as well as for reaction time and accuracy.
PET data acquisition PET scans were acquired on a Siemens/CTI ECAT EXACT HR +("962") camera (Knoxville, TN, USA;  (Spinks et al. , 2000) ) in 3D mode. Ten-minute transmission scans for attenuation and scatter correction were obtained prior to the dynamic emission scans using a rotating 68 Ge rod source. Each dynamic acquisition was 90. 5 min long and consisted of 24 frames of increasing length (1×30 s (s), 4×15 s, 4×60 s, 2×150 s, 10×300 s, 3×600 s). [ 11 C]Ro15-4513 was injected as an intravenous bolus injection of ~440 MegaBequerels (MBq; median 441 MBq, range 430-452 MBq; Table  1 ) at 30 s after the dynamic scan start. Participants were scanned on two separate days with a median interval of 12 days (range 7 -59 days; Table  1 ). The head position was maintained throughout and monitored with the camera's positioning laser.
To compensate for minor head movements during the dynamic scans, we used a post hoc frame-by-frame realignment method, as described later ("PET data quantification" section). Sixty-three transaxial images were acquired per frame. Data were reconstructed using Fourier rebinning (FORE;  (Defrise et al. , 1997) ) and 2D filtered backprojection (FBP: ramp filter, kernel 2. 0 millimetres (mm) Full Width at Half Maximum (FWHM)). The voxel size of reconstructed images was 2. 092 mm×2. 092 mm×2. 42 mm and the axial (on-axis) resolution 5. 6 mm  (Spinks et al. , 2000).
The plasma-over-blood ratio model and the metabolite model were fit for each scan, so that the whole blood measurements between 0 and 15 min could be corrected for plasma-over-blood ratio and parent fraction, respectively. The plasma-over-blood ratio model used was as follows: x x t x t 1 - + • ( / ) +1 s s x 1 2 3 4 (1) [where t s is the time from scan start in hours and x 1 to x 4 are the model parameters]. The same sigmoidal model function was used to describe the fraction of parent [ 11 C]Ro15-4513 remaining in plasma, as in  (Myers et al. , 2016). Continuous ppIFs were derived using Clickfit in-house software running in MATLAB R2014a (The MathWorks, Natick, MA, USA), as described in detail in previous studies  (Hammers et al. , 2007b; Hinz et al. , 2007) : 1) Cross-calibration of continuous and discrete whole blood radioactivity concentration measurements (4, 6, 8, 10 min).
2) Multiplication of the cross-calibrated whole blood measurements (0-15 min) by the sigmoid function obtained from fitting the model to the plasma-over blood ratio. 3) Combination of the resultant continuous plasma radioactivity concentration curve (0-15 min) with the discrete plasma radioactivity concentration measurements  (20, 35, 50, 65, 80  and 90 min) using spline interpolation. 4) Correction for parent radiotracer fraction by multiplication of the resultant continuous plasma radioactivity concentration curve (0-90 min) by the sigmoid function obtained from fitting the model to the parent fraction. 2. 5. MRI data acquisition, analysis and generation of regions-ofinterest (ROIs) All participants had 3D T1-weighted MRI scans with approximately millimetric isotropic voxel sizes on a Philips Intera 3 Tesla (3 T) MRI scanner (Philips, Best, The Netherlands) at the Robert Steiner MRI Unit, Hammersmith Hospital, for use during co-registration and region-of-interest (ROI) delineation.
There was no gross structural abnormality on any of the T1-weighted images. To isolate the grey matter in each brain, the T1-weighted images were segmented into tissue classes using SPM8 software (Statistical Parametric Mapping, Wellcome Trust Centre for Neuroimaging, University College London, London, www. fil. ion. ucl. ac. uk/spm) running in MATLAB R2014a. This process yielded grey matter probability maps for each participant, which were thresholded at 0. 5 probability (an arbitrary value which was selected as a trade-off between over-exclusion of grey matter and over-inclusion of white matter). To delineate the ROIs in each brain, the T1-weighted images were also anatomically segmented using MAPER (multi-atlas propagation with enhanced registration  (Heckemann et al. , 2010)). Using highdimensional image registration, 30 MRI data sets, each associated with manually determined labels of 83 regions  (Hammers et al.
, 2003; Gousias et al. , 2008) , were propagated to the target brain. Label fusion was used to obtain an image which consisted of 83 labelled ROIs in target space  (Heckemann et al. , 2006). Each participant's T1-weighted image and corresponding MAPERderived individual anatomical segmentations, as well as individual grey matter probability images, were co-registered with the corresponding processed PET summation image for test and retest scans separately, using SPM8. For the cortical ROIs, the individual atlases in PET space were then multiplied with the thresholded grey matter probability maps using Analyze 8. 1 imaging software  (Mayo Clinic 2002). The output grey-matter-masked, labelled ROI images were then used to sample the dynamic or parametric PET images.
PET data quantification Where required (one of 10 datasets, based on a maximum translation > 5 mm as estimated using SPM "Realign" function), the dynamic PET images were de-noised and corrected for movement frame-byframe using wavelets in Piwave 8. 0  (Studholme et al. , 1997; Turkheimer et al. , 1999)  running in MATLAB R2014a. The frame starting at 150 s (frame 6) was used as reference due to its high signalto-noise ratio and the likelihood that the participant had remained still during the first three minutes of the scan. The frames 1-5 were not corrected due to their low signal to noise ratio. The remaining frames (6-24) were automatically resliced and re-concatenated with frames 1-5 into a new dynamic image  (Hammers et al. , 2007b). Summation images (also known as 'add' or 'static' images) were created for frames 1-24, frames 16-21, and frames 22-24 with correction for 11 C radiodecay using MICKPM (Modelling, Input functions, and Compartmental Kinetics -Parametric Maps) version 5.
4 software (available on request from Rainer Hinz, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK), which itself uses MATLAB R2009bSP1. The summation images were required for calculation of global radioactivity concentrations, for use as the reference image during co-registration of the T1-weighted MRI data, and as the input for calculation of SUV images. A binary mask of the brain, which encompassed approximately 9 mm beyond the outer cortical boundary, was also created semi-automatically using Analyze 8. 1 (Mayo Clinic, Rochester, New York) for use during the subsequent generation of parametric images. Quantification of binding was performed as described below, using the same ROIs for each variant. We used a 64-bit PC (Intel Core i5 5300U CPU 2. 30 GHz, 8. 0 GB RAM) with Windows 7 Enterprise operating system.
Weighting of ROI and voxel TACs For each participant, all ROI/voxel TACs (not yet corrected for decay-correction) were weighted by the same values which were calculated according to: w L T = for frame (i = 1, 2, 3, …24; non -decay corrected data) i i i (2) [where W iweight for frame i; L ilength of frame i (seconds); T itotal of true coincidences (per second) for frame i]. For ROI TACs, the weights were normalised to sum(weights)=24 (i. e. number of frames), thresholded to max(weight)≤2. 5, and then renormalised to sum(weights)=24. For voxel TACs, the weights were not normalised but were thresholded to max(weight)/min(weight)≤1000, according to the RPM (Receptor Parametric Mapping software) scheme  (Gunn et al. , 1998; Aston et al. , 2001). 2. 9. Reversible compartmental models, requiring arterial ppIFs MICK (Modelling, Input functions and Compartmental Kinetics) version 5.
Outcome measures and statistical analyses 2. 14. 1. Comparison of test and retest injectate data For the statistical testing we used SPSS for Windows version 22 software (IBM 2013, NY, USA). Injectate data (injected radioactivity, radiochemical purity, co-injected mass of stable ligand, and specific radioactivity at the time of injection) and global radioactivity concentration were compared between test and retest sessions using Student's paired samples t-test (for data with a normal distribution) or the nonparametric Wilcoxon signed-rank test (for data which differed significantly from the normal distribution, i. e. Kolmogorov-Smirnov test p < 0. 05).
Reliability Reliability was calculated using the intraclass correlation coefficient (ICC; (MacLennan, 1993)): ICC MSBS MSWS MSBS dfWS MSWS = - + ( × ) (10) [where MSmean sum of squares; BSbetween-subject; WSwithin-subject; and dfdegrees of freedom]. The ICC is provided to allow assessment of the reliability of the measure as a function of both within-subject variability and between-subject variability; the closer the ICC to 1, the more reliable the variant, i. e. the smaller the withinsubject variability of the parameter compared with natural betweensubject variability. ICCs were computed in SPSS using the "one-way random" model. We report the "single measures" ICC. 2. 14. 5. Regional heterogeneity Finally, the ratio of binding in the highest-binding region (hippocampus) to the lowest-binding non-reference region (occipital lobes) was calculated to allow assessment of each variant's ability to depict the known heterogeneity in α5 subunit availability across the brain.
Acknowledgments We thank the staff of Hammersmith Imanet Limited and the  Hammersmith Hospital MRI Unit  for their assistance with data acquisition and preparation. This study was supported by the  Medical Research Council (MRC) Clinical Sciences Centre  ( MC_U120085812 ). Authors affiliated with (1) are grateful for support from the  Department of Health National Institute of Health Research Biomedical Research Centre funding scheme. CJM was supported by an  MRC   Doctoral Training Account  ( 3+1 ) studentship that was awarded by  Imperial College London. MJK was supported by the  Epilepsy Society, University College London  ( UCL ),  UCL Hospitals (UCLH)  and  UCLH/UCL Biomedical Research Centre. AH was supported by an  MRC   Clinician Scientist Fellowship  ( G108/585 ) and the  Neurodis Foundation.
Data analyses z-Scores were calculated using data derived from normal cohorts (  [38-43, 46-48, 52]  and Strauss and Spreen, unpublished). They are systematically adjusted for age, as well as for sex and education level when these data are available in the normal cohorts. z-Scores less than or equal to -1. 65 are considered pathological. STATIS-TICA software (version 12. 7; Statistica, Tulsa, OK, USA) was used for further statistical evaluation as required. Where appropriate, differences in demographic and clinical data were assessed using parametric (analysis of variance [ANOVA], t tests) and nonparametric (Kruskal-Wallis H, Mann-Whitney U) tests. For categorical measures, χ 2 tests were applied. For neuropsychological tests, ANOVA for independent groups was used for the analysis of z-scores, and the nonparametric Kruskal-Wallis H test was used for the analysis of raw scores.
Acknowledgements We thank the patients and control subjects for their participation. We are grateful to  Mélanie Stackfleth ,  Barbara Jung ,  Laetitia Berly  and  Anne Botzung  for their help with organisation; to  Mathias Bilger ,  Laure Di Bitonto ,  Emmanuelle Ehrhard ,  Catherine Kleitz ,  Nadine Longato ,  Laetitia Monjoin ,  Géraldine Heim  and  Natacha Vogt  for conducting neuropsychological assessments; and to  Nick Barton  for language editing.
Funding This study was funded by  Appel à Projet Interne (API)  of the  University Hospitals of Strasbourg ,  Alsace Alzheimer 67 ,  Fondation Université de Strasbourg  and famille  Jean Amrhein , and  Projet Hospitalier de Recherche Clinique  (PHRC) inter-régional ( IDRCB 2012-A00992-41 ).
Recording Apparatus Motion capture of whole body kinematics has been conducted using a VICON system (VICON Motion Systems Inc. , Los Angeles, 8 cameras, 120 Hz). Subjects wore a tight black suit allowing sticking the VICON markers close to the subject's body. Altogether, 43 markers were placed according to the "Plugin Gait" model of the VICON system (VICON, V-1. 7). To approximate head position, four additional markers were located over the right and left temples on the back and at the front of the head. Four markers were also positioned on each obstacle and door frame. Marker position were recorded, reconstructed and labeled using VICON Nexus 2. 0 software. Eye movement recording was performed with a headmounted video eye-tracker (Mocaplab, Paris, 60 Hz, mass: 30 g). The camera was located under subject's right eye, paying attention to avoid masking an important part of subject's field of vision (Figure  2A ).
www. R-project. org). Body motion capture data were Butterworth filtered with a cutoff frequency of 15 Hz  (Bernardin et al. , 2012 , with a zero-phase procedure that did not induce lag in the signal). For each trial, three phases were considered: (1) the waiting phase, from the first fixation on the screen to the audio signal indicating the direction to follow, (2) the preparation phase, i. e. , the subject started walking (12 cm from the starting point, t = 0, Figure  2C ), and (3) the walking phase, from walk initiation to reaching 40 cm before the aimed door position or 85 cm before the screen (center, t = T, Figure  2C ). Most variables were computed only during the walking phase of the trial, unless notification in the text. All data were expressed in a right-handed space-fixed laboratory reference frame (Figures  2A, B ).
ACKNOWLEDGMENTS Expert technical assistance was provided by  Rémy Brun  and Mocaplab company. The authors are grateful to  Saddek Mohand-Said  and  Céline Devisme  for patient selection, to  Ariel Zenouda ,  Sihem Kime , and  Yunpeng Zhou  for help with data collection, and to  Anne-Fleur Barfuss ,  Nicolae Sanda , and  Norman Sabbah  for proofreading. We would like to thank the two reviewers for their valuable comments.
By analyzing this map, it has been shown that the temporal relevant sources become more stable after a latency from onset of seizures. The dynamic analysis of absence seizures also has been done in humans. In  [3] , it has been reported that the cortical activations and deactivations tend to occur earlier than thalamic responses during absence epileptic seizures. In order to attain better spatio-temporal analysis of absence epileptic seizures, a new data set was acquired at the Grenoble Institute of Neurosciences (GIN) from different layers of somatosensory cortex (the onset region of absence epilepsy) in GAERS. Our goals are to 1) model the dynamics of seizures (temporal analysis) and 2) estimate onset layers during seizures (spatial analysis). To achieve our goals, we assume that there are some hidden states (under Markovian model) during the seizures and each spike is generated condition on that the state is activated.
Immunocytochemistry. Living neurons were incubated with GluN2B antibody in culture medium at 37 °C or at 4 °C for 20 min, fixed in 4% paraformaldehyde (w/v) in PBS for 5 min and subsequently incubated for 30 min incubation in blocking solution (10% FCS in PBS, 0,1% Glycin, 0,1% Triton X-100). Further primary antibodies were incubated for 90 min at RT. After three washing steps with PBS cells were incubated with secondary antibody for 1 h at RT. Cells were washed and mounted in Mowiol. Preparations were kept at 4 °C until examination. Images were acquired on a Zeiss axioplan fluorescence microscope and processed for quantitative analyses with ImageJ (US National Institutes of Health). Quantification of synaptic GluN2B intensity was done by OpenView Software by Noam Ziv. Immunostainings of the total amount of GluN2B followed basically the same protocol but the GluN2B antibody was applied after fixation and permeabilization.
Acknowledgements We gratefully acknowledge technical assistance by  Kathrin Hartung  and  Janina Juhle. The study was supported by the  Federal State of Saxony-Anhalt (LSA RG Molecular Physiology) ,  DFG  (grant  HE-3604/2-1 ,  GRK1167 ,  FE1335  and  SFB779 A06 ,  B14 ) and the  Schram Foundation  ( T287/21796/2011 ).
Quantification of reactive oxygen species Intracellular reactive oxygen species (ROS) were assessed using the cell membrane permeable probe dihydro-rhodamine-123 (DHR-123) (#D23806; ThermoFisher Scientific, Waltham, MA, USA) according to a procedure described before  (Rousseau et al. 2013). Measurements were made at a stage where ROS production is optimal in cultures exposed to PDC, that is 24 h after initiation of the treatment. For each culture condition, fluorescent images of 6 randomly chosen fields were acquired with a 209 fluorescence objective using a Nikon TE 2000 inverted microscope (Nikon, Champigny-sur-Marne, France) equipped with an ORCA-ER digital camera and the HCimage Imaging software (Hamamatsu, Corp. , Bridgewater, NJ, USA). Results were expressed in fractional change in fluorescence intensity relative to baseline in control cultures (F/F 0 ) as described previously for other fluorescent probes  (Toulorge et al.
Image acquisition and cell quantification In the PDC paradigm, the bottom surface area of each culture well was scanned in one piece using an LI-COR Odyssey infrared imaging system (LI-COR Biosciences)  (Toulorge et al. 2011 ) and semi-automatic quantification of DA cell numbers was made with the open source Icy software  (de Chaumont et al. 2012 ). In the paradigm of spontaneous DA cell death, higher resolution composite images were generated with an Arrayscan XTi automated fluorescence microscope (ThermoScientific, Courtaboeuf, France), thus allowing both cell counting and morphometric analyses. Post-hoc image analysis was then performed with the HCStudio software (Thermoscientific). With both methods, data analysis was performed over > 60% of the surface area of each culture well. For illustrative purposes, we also used digitized images of TH + neurons that were taken with a Nikon Eclipse TE-2000 microscope, equipped with an ORCA-ER digital camera (Hamamatsu Photonics, Hamamatsu, Japan), operated with the HCI software (Hamamatsu).
Statistical analyses Data expressed as means AE SEM were analyzed, using the SigmaPlot 12. 5 software (Systat Software Inc, San Jose, CA, USA). Each data point was derived from at least three independent experiments. In the PDC model, sets of data were assessed by oneway analysis of variance followed the Student-Newman-Keuls post hoc test for all pairwise comparisons. In the spontaneous DA cell death model, data were analyzed with the Kruskal-Wallis one way analysis on ranks followed by the Student-Newman-Keuls post hoc test.
Acknowledgments and conflict of interest disclosure This study was funded by a grant from  Air Liquide Sant e International. The research leading to these results also received support from the programs ' Investissements d'Avenir '  ANR-10-IAIHU-06  and  ANR-11-INBS-0011 - NeurATRIS: Translational Research Infrastructure for Biotherapies in Neurosciences. This work benefited from equipment and services from the CELIS cell culture facility, at  ICM. More specifically, we thank  David Akbar  for assistance regarding automated fluorescence microscopy. ML, JP, and GF are Air Liquide employees. DLN was supported by a CIFRE doctoral fellowship co-funded by  Association Nationale de la Recherche et de la Technologie  ( ANRT ,  2014/0589 ) and  Air Liquide Sant e International. We also wish to mention the contribution of other  Air Liquide  staff members,  Aude Milet  and  Matthieu Chalopin  for project management and  Franc ßois Gilles  for technical support with gas delivery systems.
Design in detail Fig. 1  shows the functional blocks of the INMS system developed to achieve the design objectives. The first block comprises the computer controller devices: scanner computers (for MEG or fMRI); a computer to generate synchronisation pulses from the scanner and provide a trigger to the INMS system to initiate its stimulation sequence (using Presentation software; Neurobehavioral Systems, Berkeley, CA); and a host computer for the INMS control and signal recording. These devices are all positioned outside of the screened room of the MRI or MEG scanner, have a common ground, and are not isolated. Optical fibres pass through to the scan rooms and all pieces of equipment are isolated from ground. These provide the participant isolation and all equipment inside the scan room runs off rechargeable batteries.