dataesr/scientific-paragraphs-categorization huggingface dataset:
Power analysis An analysis was conducted to estimate the sample size necessary to achieve the recommended level of 80% power to detect a medium-sized effect (Cohen's d = 0. 50) in an ANCOVA with one covariate and 4 independent groups  [37]. To do this, we relied on G Ã Power software  [38]. The analysis yielded a sample size of 128. Thus, we decided to have at least 128 participants in our study.
Procedure and materials The experiment was conducted on a computer using Psychopy1 software  [39]. All materials and instructions were displayed in French. Body dissatisfaction. Body dissatisfaction was measured with the Body Dissatisfaction subscale of the Eating Disorder Inventory  [40]. Participants indicated their agreement/disagreement with nine statements (e. g. , "I think that my stomach is just the right size") using 7-point Likert scales (1 = completely disagree, 7 = completely agree). This construct was assessed to test whether thin-ideal exposure and cognitive load affect negative thought accessibility when body dissatisfaction is controlled for. Cognitive load manipulation. Digit retention instructions, adapted from Gilbert et al. [25] , were used to manipulate cognitive load. Participants were randomly allocated either to a low or to a high cognitive load condition, in a between-participants design.
This work was supported by the  National Agency for Research (ANR) ,  Paris, France  [grant number  ANR-13-BSH2-0008-01 ] (http://www. agence-nationale-recherche. fr/? Projet=ANR-13-BSH2-0008 ) to LS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Immunohistochemistry and Image Analysis Antibodies used in this study are listed in Table  1. For Aβ immunohistochemistry (IHC), sections were pretreated with 80% formic acid for 3 min and were permeabilized with 0. 2% Triton X-100/sodium phosphate buffer. Sections were then blocked with 10% ''Mouse On Mouse'' Kit serum (Vector Laboratories) for 1 h before incubation with mouse biotinylated anti-Aβ antibody (6E10) at 4 • C overnight. After washing in PBS, the sections were incubated with the ABC kit (Vector Laboratories) for 2 h and developed using DAB (Sigma). Images were acquired using Leica ICC50 HD microscope. Quantification of the 6E10 staining intensity was performed using Mercator software (Explora Nova, Mountain View, CA, USA). The number of plaques, the average plaque size and the plaque burden, expressed as percentage of analyzed area, were calculated in the cortex and hippocampus of the APP/PS1 mice.
Sections were then incubated with streptavidin Alexa Fluo 488 conjugate (1/1000; Thermo Fisher Scientific). Sections were finally incubated with DAPI (1/5000; Sigma-Aldrich) for 10 min and treated for 10 min in 0. 3% Suden Black (Sigma-Aldrich) with 70% ethanol to block autofluorescence. Images were acquired using a Zeiss LSM 710 confocal laser-scanning microscope to define co-localization of A 2A R with glial markers and amyloid plaques. 3D reconstruction of 2D confocal z stacks was performed using Imaris software (Bitplane, South Windsor, CT, USA).
Immunoreactivity was visualized using chemiluminescence kits (ECL TM , Amersham Bioscience) and a LAS3000 imaging system (Fujifilm). Results were normalized to GAPDH and quantifications were performed using ImageJ software (Scion Software). mRNA Extraction and Quantitative Real-Time RT-PCR Analysis Total RNA was extracted from hippocampi and cortex, and purified using the RNeasy Lipid Tissue Mini Kit (Qiagen, France). One microgram of total RNA was reverse-transcribed using the Applied Biosystems High-Capacity cDNA reverse transcription kit. Quantitative real-time RT-PCR analysis was performed on an Applied Biosystems Prism 7900 System using Power SYBR Green PCR Master Mix. The thermal cycler conditions were as follows: hold for 10 min at 95 • C, followed by 45 cycles of a two-step PCR consisting of a 95 • C step for 15 s followed by a 60 • C step for 25 s.
) at a concentration of 1. 1 × 10 7 microspheres/mL for 60 min at 37 The statistical analyses were performed with Genespring software version GX13. 0 (Agilent Technologies). Microarrays have been normalized to the 75th percentile. Probes below background under all conditions were removed from the analysis. Differentially expressed genes were identified by a moderated t-test with correction of multiple tests by the Benjamini Hochberg (BH) method. We selected significantly deregulated probes with a corrected p-value of less than 0. 05 and with an expression differential of at least 1. 5× (FC1. 5). Differentially expressed probes were further analyzed in term of molecular function and biological process using the Ingenuity Pathways Analysis (Qiagen Inc. ) software.
Datasets were tested for significance with the One-way ANOVA and represent data from three independent experiments. * * * p < 0. 001 vs. WT (F) As measured by ELISA, MSX-3 treatment decreased Aβ1-42 levels in the cortex of APP/PS1 mice while Aβ1-40 levels was found increased. Overall, the Aβ1-42/Aβ1-40 ratio was found significantly reduced by the A 2A antagonist treatment ( * p < 0. 05, * * p < 0. 01 vs. APP/PS1 H 2 O using Student's t-test; N = 7-11/group). (G) Western blot analysis performed in cortex of water and MSX-3 treated APP/PS1dE9 mice did not revealed any change in APP and Carboxyterminal fragments (CTFs) expression (N = 6/group). Results are expressed as mean ± SEM.
ACKNOWLEDGMENTS We thank the  Bio Imaging Center Lille Nord de France (Campus Hospito-universitaire ) as well as  A. Bongiovanni  and  M. Tardivel  for access microscopes and imagery analysis platform. We thank the animal core facility (animal facilities of  Université de Lille) of Plateformes en Biologie Santé de Lille  as well as  M. Besegher-Dumoulin ,  C. Declerck ,  J. Devassine ,  Y. Lepage ,  C. Meunier ,  C. Degraeve  and  D. Taillieu  for transgenic mouse production and care. Our laboratory is also supported by the  France Alzheimer, FHU VasCog research network (Lille, France) ,  and programs d'investissements d'avenir LabEx (excellence laboratory) Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease (DISTALZ) ,  ANR  ( ADORATAU, SPREADTAU, GRAND ),  Fondation pour la Recherche Médicale ,  Fondation Plan Alzheimer ,  Inserm ,  CNRS ,  Université de Lille, Lille Métropole Communauté Urbaine ,  Région Hauts-de-France (COGNADORA) ,  DN2M.
FUNDING This work was supported by a cross-border grant from  LECMA/Alzheimer Forschung Initiative/Vaincre Alzheimer  (to DB and CM). We hereby thank  Frédéric Leprêtre  for submitting transcriptomics data to GEO. EF and KC are supported by  Université de Lille , LC was supported by  Italian Society of Pharmacology and LabEx DISTALZ.
Signals exceeding the upper level of the window (crossing the lower and upper levels) were not considered. Multiplexing the input signal and window discriminators provided convenient oscilloscope visualization and ease of setting up the experiment. It eliminated adjusting the oscilloscope levels for drift. In absence of any movement, only metabosensitive afferent fibers exhibiting spontaneous tonic low frequency baseline activity were active  (Decherchi et al. , 1998). Thus, metabosensitives afferent activities were selected according to their action potential amplitudes which were higher than the background noise. The output provided noise-free tracings (discriminated units) on which action potentials were displayed on a computer and then counted using data analysis system (Biopac MP150 R and AcqKnowledge R software, United States) at 1s intervals (in Hz).
Statistics Data processing was performed using Sigmaplot R 14 SPSS. Data were expressed as mean ± SEM. Differences were tested by two-way analysis of variance (ANOVA test, factors: group x timing) completed by a Student-Newman-Keuls post hoc test to compare the metabosensive afferent responses to KCl and LA injections during aging process and after treadmill training (factors: age x doses). One-way ANOVA were used to compare EIF and muscle properties during aging process, while t-test were used to evaluate the differences after the training protocol. Results were considered statistically significant if the p-value fell below 0. 05.
Parasympathetic firing-rate recordings Firing-rate activities were recorded at the thoracic branch levels of the vagus nerve (parasympathetic) along the carotid artery in overnightfasted rats as described previously  [19]. Briefly, parasympathetic nerves were placed on a pair of recording silver wire electrodes (0. 6-mm diameter), and action potentials were displayed and saved on a computer after initial amplification through a low-noise amplifier (Bio Amp, ADInstruments). Unipolar nerve activity was recorded continuously during 15 min (baseline) and then 15 min after glucose injection (ip, 3 g/kg). Data were digitized with digitizer PowerLab/4sp. Signals were amplified 105 and filtered at low-and high-frequency cut-offs of 100 and 1,000 Hz and monitored with Chart 4 program (ADInstruments).
Nonfixed brain sections were incubated for 2 hours at 37 °C in a humid dark chamber in a reaction buffer that contained 0. 5 M Tris-HCl, 1. 5 M NaCl, 50 mM CaCl 2 , 2 mM sodium azide (pH 7. 6), and 80 μg/ml of intramolecularly quenched FITC-labeled DQgelatin (EnzCheck collagenase assay kit; Thermo Fisher Scientific, Waltham, Massachusetts, USA). After the incubation, the tissue was fixed in 4% paraformaldehyde Antigenfix solution (Diapath, MM France, Brignais, France), incubated for 5 minutes with 0. 5 μg/ml Hoechst 33258 (Thermo Fisher Scientific), and mounted in Prolong Gold Antifading reagent (Thermo Fisher Scientific). The sections were incubated with 1 mM phenanthroline (Thermo Fisher Scientific), a broad-spectrum metalloproteinase inhibitor. Samples were observed with a confocal microscope (LSM 700 Zeiss, Jena, Germany), and images were analyzed using the Zen (Zeiss) and ImageJ softwares (NIH, Bethesda, MD, USA).
Statistical Analysis The analyses were performed using SPSS software v21 (SPSS Inc. , Chicago, IL, USA). The data were analyzed using the Kruskal-Wallis test, Mann-Whitney U test, or Student t-test with winsorizing for variables with nonnormal distributions, ANOVA, and an independent sample t-test for normal distributions. Levene's test for equality of variance was performed before the t-test. If Levene's test documented a significant variance difference between the compared groups, a t-test assuming different variances was performed. Otherwise, a t-test assuming equal variance was performed. All the differences were considered significant if p < 0 05. When calculating the results of the histopathological evaluation, chi-square test without Yates' correction was performed.
Acknowledgments The NACA used was a kind gift of  Dr. Glenn Goldstein ,  New York, NY, USA. The authors thank  Ms. Eliane Charrat  for her technical support and  Professor Sandvik  for the advice on statistical analysis. The authors are also grateful for the assistance from  Vivi Stubberud ,  Aurora Pamplona ,  Sera Sebastian ,  Leonid Pankratov ,  Bjørn Petter Benterud ,  Grethe Disease Markers Dyrhaug ,  Monica Atneosen-Åsegg , and  Ashley Kim. The study was funded by  Helse-Sør Øst (South and Eastern Norway Regional Health Authority ; source number 6051; Project no. 39570 ).
3-D reconstruction A Nikon Eclipse E800 microscope equipped with a digital color camera (Optronics, USA) was used to visualize mounted cerebellar sections under brightfield illumination. The contour of every 4 th section was then manually drawn using Microfire software (Neurolucida, MBF Bioscience, USA) and cell counts were performed. The sections were then aligned and stacked (160 µm spacing).
Electrophysiology Multi-taper Fourier analyses (Chronux toolbox  [85] ) were used to calculate power and coherence of the LFP data. We used a 10s sliding window (9s overlap) and 19 tapers for all analysis, except for the example presented in Fig. S6  in which we used a 2s sliding window (1. 5s overlap) and 3 tapers. Statistical comparison was restricted to the 6-12 Hz frequency band unless otherwise stated. For recordings made in the home-cage environment, LFP data were manually selected, as described above for behavioral data. Analysis was focused on LFP gathered during periods of active movement. Selected data were then filtered to remove any large-amplitude, low frequency artifacts using a stationary wavelet transform  [86]. Spectral power and coherence were then calculated. The spectral power between 0.
1 and 45 Hz was z-scored due to interanimal variations in LFP magnitude. Mean power and coherence were calculated in the 6-12Hz frequency range for all cerebello-hippocampal combinations. Data duration in the home-cage environment varied across mice (range, 12 to 132 min). Therefore, to reduce the impact of data length on subsequent analyses and also to match with subsequent linear track experiments (duration of 12 min), for each mouse we concatenated the LFP in to 12 min blocks. When multiple 12 min blocks were available (number of data blocks ranged from 1 to 11) we calculated the average coherence across all blocks. The number of 12 minute blocks used was found to have no correlation with the overall level of calculated coherence (See Fig. S5F ). For RW and virtual linear track recordings, general electrophysiological analysis methods were the same as described for home-cage recordings including artifact removal procedures (in this case MFB stimulation artifacts were also removed using the methodology described in  [86] ).
Statistical analysis Statistical analyses were conducted using Matlab Statistical Toolbox and Prism (Graphpad, USA). Normality was assessed using a Shapiro-Wilk test. Parametric and non-parametric tests were then used accordingly. Significant changes in coherence were observed over trials (combination x trial two-way ANOVA with FDR correction, trial effect p < 0. 001, combination effect p = 0. 0285, interaction effect p < 0. 0001). Solid green rectangles indicates trials where Crus Ihippocampus coherence was higher than those in trial 1 (q < 0. 05). This increase in Crus Ihippocampus coherence was also reflected in significant changes relative to other lobules: grey bordered rectangle corresponds to trials significantly higher than lobule II/IIIhippocampus coherence (q < 0. 05) while red bordered rectangles indicates those significantly higher than lobule VI-hippocampus coherence (q < 0.
Acknowledgements This work was supported by the  Fondation pour la Recherche Médicale   DEQ20160334907-France , by the  National Agency for Research   ANR-17-CE16-0019-03  (LRR), by the  CNRS and Aix-Marseille Université  through  UMR 7289  (PC). This work also received support under the program Investissements d'Avenir launched by the  French Government  and implemented by the  ANR , with the references,  PER-SU (LRR)  and  ANR-10-LABX-BioPsy (LRR). The group of LRR is member of the  Labex BioPsy and ENP Foundation. Labex are supported by  French State  funds managed by the  ANR  within the  Investissements d'Avenir programme  under reference  ANR-11-IDEX-0004-02. We thank  Roxanna Ureta  for help with histology,  Lilith Sommer  for help with behavioral experiments,  Gregory Sedes  and  Nadine Francis  for help developing analysis codes, and  Richard Apps  for his insightful comments.
In FEP subjects, no such associations were noted. GABA-A receptor antibodies, which have been proposed as NSAbs with emerging disease-relevance, showed no phenotypic associations. Data on the predictive utility of NSAbs in UHR and FEP subjects will be presented. Discussion: With appropriately fine-grained phenotyping and careful consideration of moderating biological factors and assay variation, clear disease-relevance of NSAbs could be established in UHR subjects but not in FEP subjects. In particular, NMDAR antibodies may have important biomarker potential in the at-risk mental state. The failure to establish clear disease-relevance in previous psychiatric cohorts may reflect a genuinely irrelevant antibody but could also be due any of the following:.
The goal of the paper is to present an example of integrated analysis of electrical, hemodynamic, and motor activity accompanying the motor function recovery in a poststroke patient having an extensive cortical lesion. The patient underwent a course of neurorehabilitation assisted with the hand exoskeleton controlled by brain-computer interface based on kinesthetic motor imagery. The BCI classifier was based on discriminating covariance matrices of EEG corresponding to motor imagery. The clinical data from three successive 2 weeks hospitalizations with 4 and 8 month intervals, respectively were under analysis. The rehabilitation outcome was measured by Fugl-Meyer scale and biomechanical analysis. Both measures indicate prominent improvement of the motor function of the paretic arm after each hospitalization.
Patient The patient, female, 42 years old, was recruited in the study 16 months after hemorrhagic stroke. Following the MRI data the lesion was located in cortical-subcortical areas of the frontal lobe in the left hemisphere (Figure  3A ). The muscle tone and tendon reflexes of the paretic arm were increased. The patient was able to follow the instructions of the rehabilitation procedure (the score of Montreal Cognitive Assessment amounted to 26) and had no other neurologic, neuromuscular or orthopedic diseases. The muscle force was assessed by Medical Research Council (MRC) scale as 1 for distal domain of the arm and as three for proximal one during all the three hospitalizations. Anxiety and depression were assessed by Hospital Anxiety and Depression Scale (HADS)  (19)  as 4/4 for first and second hospitalizations which corresponds to the norm (<7) and as 8/7 for the third one, which corresponds to subclinical anxiety/depression.
BCI+ Exoskeleton Procedure During the BCI treatment the patient was sitting in a medical chair with her hands placed into two exoskeletons (Android Technics, Russia). She controlled extension of two hand exoskeletons (Figure  1A ) by performing motor imagery tasks, following visual cues presented on the monitor (Figure  1B ). The tasks were to relax (R) and to imagine kinesthetically slow extension of either the paretic (right, RH) or the intact (left, LH) hand fingers. The exoskeleton extended the patient's fingers if the BCI classifier recognized the imagery of their extension. The EEG data were recorded with NVX52 device (Medical Computer Systems, Russia) with 40 AgCl electrodes. The data were digitized at 500 Hz, filtered on-line with band-pass 5-30 and 50 Hz notch filters, and fed to the BCI classifier.
The raw EEG data were stored for further processing. Each day electrode positions on the scalp were digitized with the EEG PinPoint system (Localite GmbH, Germany) after the daily BCI session was over. The positions were set in the MRI anatomical image coordinate system. The BCI classification algorithm was designed under the assumption that EEG has a multivariate Gaussian distribution with zero mean and covariance matrix depending on the task performed  (22). The BCI classified ongoing EEG signal by comparing its covariance matrix obtained for the last second epoch with the three EEG covariance matrices corresponding to the R, LH, and RH tasks (Figure  1C ). The details of the experimental setup and procedure are given in  (8).
MRI and fMRI Registration and Analysis Scanning was performed on a 3T Siemens Magnetom Verio at the beginning and at the end of third hospitalization. The anatomical images were acquired using T1-weighted multiplanar reconstruction mode (TR = 1,900 ms, TE = 2. 47 ms, 512 × 512 matrix, FOV = 250 × 250 mm) with 0. 488 × 0. 488 × 1 mm voxel size. The fMRI data were acquired with BOLD sensitive T2 *weighted mode (36 slices, TR = 3,000 ms, TE = 30 ms, 64 × 64 matrix, FOV = 192 × 192 mm, BW = 2,232 Hz/pixel) with 3 × 3 × 3 mm voxel size. 120 3D images were obtained during each session (one session before and one after training). Experimental design contained consequent trials of the mental tasks used to control BCI. Each trial lasted 30 s and contained 10 full brain scans. The fMRI data were processed according to SPM12 (Statistical Parametric Mapping, Wellcome Trust Centre for Neuroimaging at UCL, London, UK) standard single subject processing workflow.
BCI Biomarkers The data of 72 EEG recordings were processed, 24 recordings for each hospitalization. The component clusters were sorted according to the number of elements. The components of each of the first four clusters appeared in more than 65% sessions while the components of all other clusters appeared in <20% sessions. Moreover, the components of each of the four clusters were relevant for the BCI performance in more than 50% of sessions while the components of all the rest clusters were relevant in <40% of sessions. The component relevance means that changes in their activity are the main cause for the difference between the task-specific EEG covariance matrices. That is why the components of the first four clusters, which were the most common and relevant, were considered as the main BCI biomarkers and were selected for detailed analysis.
In patients with pharmaco-resistant focal epilepsies investigated with intracranial electroencephalography (iEEG), direct electrical stimulations of a cortical region induce cortico-cortical evoked potentials (CCEP) in distant cerebral cortex, which properties can be used to infer large scale brain connectivity. In 2013, we proposed a new probabilistic functional tractography methodology to study human brain connectivity. We have now been revisiting this method in the F-TRACT project (f-tract. eu) by developing a large multicenter CCEP database of several thousand stimulation runs performed in several hundred patients, and associated processing tools to create.
, 2005; David et al. , 2013; Krieg et al. , 2017; Lacruz et al. , 2007). Connectivity studies from iEEG often suffer from a small spatial sampling available in a single patient, number and coverage of recording electrodes are limited and mainly focused on the epileptic network of interest. In addition, the connectivity estimated from CCEP depends on the stimulation parameters  (Donos et al. , 2016b) , which largely differ between clinical epilepsy centers: for instance, current intensity between 0. 5 and 4 mA is used for SEEG, and between 8 and 15 mA for ECoG, with pulse duration ranging from 1 to 3 ms and pulse shape can be biphasic or monophasic. A multicenter database in a large number of patients should help avoiding these two limitations when inferring the dynamical properties of a large number of possible cortico-cortical connections.
So far, CCEP studies are limited regarding these two points as they involved only 1 or 2 centers and described small series of patients from 5 to 25 patients  (Boido et al. , 2014; Conner et al. , 2011; Donos et al. , 2016a; Entz et al. , 2014; Keller et al. , 2014a; Krieg et al. , 2017; Matsumoto et al. , 2007). Following our initial study aiming at going beyond these limitations with a probabilistic approach developed with a small dataset of 35 patients  (David et al. , 2013) , we started a multicenter project (F-TRACT, f-tract. eu) aiming at developing a large CCEP database for describing the spatial-temporal properties of cortico-cortical connections at an unprecedented precision in humans. The aim of this report is to present a snapshot of the methods and data of the F-TRACT project with an extended pool of 213 patients.