dataesr/scientific-paragraphs-categorization huggingface dataset:
METHODS Standard protocol approvals, registrations, and patient consents. This study was approved by the ethics committee of the University Hospital of Toulouse, France ("Comite Consultatif pour la Protection des Personnes," CHU Toulouse, ID-RCB: 2012-A00009-34). Written informed consent was obtained directly from the healthy volunteers and from the legal surrogate of the patients. Clinical trials identifier: NCT01620957. Participants. This multicenter study was undertaken in 4 intensive critical care units affiliated with the University Teaching Hospital (Toulouse, France) between January 2013 and February 2014. We compared 31 patients with severe brain injury who met the clinical definition of coma (Glasgow Coma Scale score at the admission to hospital ,8 with motor responses ,6; 16 patients with traumatic and 15 with anoxic brain injury; age range: 23-51 years) with 10 age-matched healthy volunteers (range: 24-45 years).
All patients were prospectively recruited and managed according to standard of care recommendations. 11 To avoid confounding factors, all patient assessments were conducted at least 2 days (4 6 2 days) after complete withdrawal of sedative drug therapy and performed under normothermic conditions. In patients, standardized clinical examination was performed by raters blinded to neuroimaging data (Glasgow Coma Scale  12  and the Full Outline of Unresponsiveness 13 on the day of scanning and 3 months later using the Coma Recovery Scale-Revised  14  ). Table  1  reports demographic and clinical characteristics of the patients. Imaging procedures. Acquisitions. In all participants, 11 minutes of resting-state fMRI was acquired on a 3T magnetic resonance scanner (Intera Achieva; Philips, Best, the Netherlands).
15 The structural integrity of these seeds was guaranteed by visual assessment on T1 imaging. Four patients (3 with traumatic and 1 with anoxic brain injury) showed significant anatomical anomalies in these regions and were excluded from further analysis. In the remaining participants of the cohort, the T2* mean values of voxels located in the seed region were extracted from the T2* mean images and 2-sample t tests were performed to compare values measured in the control and patient groups; no significant difference was observed between the groups (table e-1). Preprocessing. Data analysis was performed using SPM8 (http://www. fil. ion. ucl. ac. uk/spm/) and the CONN-fMRI functional connectivity toolbox, version 13. p (http://www. nitrc. org/projects/conn). 16 Echo-planar images were realigned, coregistered, and normalized to the Montreal Neurological Institute echo-planar imaging template, and smoothed using an 8.
To control for possible effects related to circadian cycles, all trials were performed between 10 am and 3 pm every day  (Menard and Quirion, 2012b; Menard et al. , 2014b). Data derived from the MWM task were computerized using an HVS image video tracking system (Buckingham, UK) for the LE rats (DMHUI phenotyping platform) or a video tracking system from Panlab (Harvard Apparatus, Barcelona, Spain) for the LOU rats (CHUM Research Center platform).
Statistical analysis Data are expressed as the mean AE standard error of the mean. Twoway analysis of variance (ANOVA) followed by Bonferroni post hoc test was used to assess the level of significance between groups for MWM learning acquisition, time spent in the target quadrant, swim speed, and total distance traveled during the probe tests. Significance for average latency on day 5 was evaluated with 1-way ANOVA and Bonferroni post hoc test. Comparison between groups and each receptor subtypes (AMPA, NMDA, and group I mGluR) or VGLUT levels was performed with 2-way ANOVA followed by Bonferroni post hoc test. Significance for ratios was evaluated with 1-way ANOVA (and Bonferroni post hoc test). Multivariate analysis of variance (MANOVA) was used to test the effect of strain on the variables studied  (Sugimoto et al.
Acknowledgements This research was supported by grants from the  Canadian Institutes of Health Research (CIHR)  to  Rémi Quirion  ( 9437-37 ) and  Salah E. L. Mestikawy  ( ANR-10-MALZ-0105 ) and from the  Quebec Network for Research on Aging  (a network funded by the  Fonds de Recherche du Québec -Santé)  to  Pierrette Gaudreau ,  Guylaine Ferland , and  Salah El Mestikawy. Caroline Ménard  was the recipient of a postdoctoral fellowship from  CIHR.
• The study of their swimming behavior at single cell and population levels unveils properties of their sensorimotor system. • Microrobots operating in a low Reynolds number regime would benefit from the understanding of their living counterparts. The density of cells (A) is monitored through the gray level intensities: As a circular area is exposed to constant light, we measure: • The sensitivity and the dynamic range of the algae from the density at the center (Ac) for various light intensities. • The spatial resolution (s) as the slope on the border of the algae bump. • The formation time where the bump to reach maximal resolution and its persistence time. Other data from microscope recordings allows to get independent estimations for parameters of the model using single cell trajectories and population dynamics.
However, there are also important differences. Group 3 MBs show high levels of MYC, and often gain of chromosome 1q and/or loss of chromosome 5q and 10q, whereas Group 4 MBs have low levels of MYC and MYCN and loss of the X chromosome in 80 % of females within the subgroup  [445]. In summary, B-omics^datasets on large series of MB combined with the results of ontogeny studies performed in mouse models aiming at characterizing the cell of origin of the various subgroups together with magnetic resonance imaging (MRI) studies in patients lend additional support to the notion that MB is in fact a disease arising from deregulated cellular and molecular mechanisms involved in the development and homeostasis of the cerebellum.
Acknowledgments We dedicate this Consensus Paper to the memory of  Professor Ferdinando Rossi , a brilliant neuroscientist and a passionate expert on cerebellar development. We apologize to all our colleagues whose work has not been cited due to the focus and space constraints of this review. Arancillo and Sillitoe: R. V. S is supported by funds from  Baylor College of Medicine and Texas Children's Hospital, The Bachmann-Strauss Dystonia and Parkinson Foundation, Inc. ,  Baylor College of Medicine IDDRC  grant  1U54HD083092-01 , and  NINDS  grant  1R01NS089664-01. M. A. is supported by a postdoctoral award from the  National Ataxia Foundation (NAF). Becker: E. B. is the recipient of a  Research Fellowship  from the  Royal Society. Dobyns and Millen: This work is supported by  NIH  grants  NS050375  to  W.
B. D. and  NS080390  to  K. J. M. Hoshino : I thank  Satoshi Miyashita  for drawing Fig. 2. Leto: This work was supported by grants from  University of Turin and Research Fund  for the  Promotion of Basic Research Grant   RBFR10A01S. Joyner: I thank my lab members (present and past) for stimulating discussions and  Emilie Legué  for helpful comments on the manuscript and for drawing Fig. 11e. Our cerebellar research is supported by grants from the  NIH  ( R37MH085726  and  NS092096 ). Kilpatrick: We thank  Mr. George Gagnon  for his invaluable and gracious assistance in all aspects of this work. This work was supported by  Public Health Services  grant  NS063047. Sekerková: This work was supported by  NIH  grant  RO1 09904.
Statistics First, each DTI and electrophysiological metric was independently compared between groups using unpaired t tests. Then, the metrics found to be significantly modified in patients were retained for further analyses to test the relationships between these metrics and clinical features for the group of participants. Thus, multiple regression analyses were performed to test the link between λ ⊥ , MD, N9, N20 and CVc according to the subject groups. In addition, Pearson correlation analyses were performed to investigate the relationships between clinical features and λ ⊥ , MD, N9 and N20. The 95% CI of control data was calculated for DTI and electrophysiological metrics. These intervals were used to fix the limits of normal values in order to identify the patients exhibiting abnormal data with a risk of error less than 5%.
Acknowledgements The authors thank  Dr N Leforestier ,  Dr T Lenglet ,  Dr F Salachas ,  Dr G Bruneteau  and  Dr V Meininger  for their help in patient recruitment, and  Dr R Morizot-Koutlidis  for her help in the experimental design for SEP recordings. They also thank  C Macia ,  K Nigaud ,  F Humbert  and  R Valabrègue  for their valuable technical assistance during MRI acquisitions, which were performed at  CENIR-ICM Hôpital Pitié-Salpêtrière. Their gratitude also goes to  S Blancho  for her help to obtain the approval of the local ethics committee and for monitoring the project. At last, they deeply thank  Dr Anna Hudson  ( Neuroscience Research Australia ) for scrutinising the English. Contributors VM-P and P-FP were involved in conception and design of research and P-FP in patient selection.
Funding This work was supported by  IRME  ( ID RCB 2012-A00016-37 ) and  ANR  ( ANR-12-JSV4-0007-01 ). CI was supported by a grant from  FRM ,  M-MElM  by  IHU-A-ICM Pitié-Salpêtrière  ( ANR-10-IAIHU-06 ), and SS by a grant from  UPMC.
Enhanced neural encoding of threat-signaling emotions To validate the finding of enhanced sensitivity to threat-signaling emotions, and to identify its neural substrates, we then investigated how facial expressions modulated scalp EEG activity recorded during the emotion categorization task. Instead of computing event-related averages, we relied on a parametric regression-based approach consisting in regressing single-trial EEG signals against the strength of the displayed emotion at each electrode and time point following the presentation of the face  (Wyart et al. , 2012a (Wyart et al. , , 2015)). A general linear regression model (GLM) was fit to the EEG data where emotion strength (from 0 for a neutral/emotionless expression to 7 for an intense fear/ anger expression) was introduced as a trial-per-trial predictor of broadband EEG signals at each electrode and time point following stimulus onset (from 0.
2, SD = 7. 8). These scores are comparable to the original published norms for this age group  (Spielberger, 1983)  and to those from French normative data  (Bruchon-Schweitzer and Paulhan, 1993). We analyzed the effect of anxiety on the behavioral and neural data in two complementary ways: 1. by splitting the participants in two equally-sized groups based on their measured anxiety, and 2. by correlating neural encoding parameters estimated at the level of individual participants with their measured anxiety. Surprisingly, we found no effect of anxiety on overall measures of performance (t 11 < 0. 05, p>0. 9), nor on the difference between THREAT+ (anger direct and fear averted) and THREAT-(anger averted and fear direct) combinations of gaze and emotion (F 1,22 < 0. 4, p>0. 5). Nevertheless, the absence of effect of anxiety at the behavioral level was accompanied by a compensatory double dissociation in the neural data.
Stimuli Stimuli consisted of 36 identities (18 females) adapted from the Radboud Faces Database  (Langner et al. , 2010)  that varied in emotion (neutral, angry or fearful expressions) and gaze direction (direct toward the participant or averted 45˚to the left or right). Using Adobe Photoshop CS5. 1 (Adobe Systems, San Jose CA), faces were modified to remove any visible hair, resized and repositioned so that eyes, nose and mouth appeared within the same circumference. All images were converted to greyscale and cropped into a 280 x 406 pixel oval centered within a 628 x 429 pixel black rectangle. To vary the intensity of emotional expressions, faces were morphed from neutral to angry expressions and from neutral to fearful expression using FantaMorph (Abrosoft http://www. fantamorph. com/).
Model selection We performed model-guided analyses of the behavioural data to characterize the observed increase in recognition accuracy for THREAT+ combinations of gaze and emotion. We used Bayesian model selection based on the model evidence (estimated by a 10-fold cross-validation estimation of model log-likelihood, which penalizes implicitly for model complexity without relying on particular approximations such as the Bayesian Information Criterion or the Akaike Information Criterion). We applied both fixed-effects and random-effects statistics previously described in the literature. The fixedeffects comparison assumes all participants to have used the same underlying model to generate their behavior, such that the overall model evidence for a given model is proportional to the product of model evidence for the model for all participants.
EEG acquisition and pre-processing An EEG cap of 63 sintered Ag/AgCl ring electrodes (Easycap) was used to record EEG activity. EEG activity was recorded at a sampling rate of 1000 Hz using a BRAINAMP amplifier (Brain Products, BRAINAMP MR PLUS) and low pass filtered online at 250 Hz. The reference channel was placed on their nose and a forehead ground was used. Impedances were kept under a threshold of 10 kW. The raw EEG data was recalculated to average reference, down-sampled to 500 Hz, low-pass filtered at 32 Hz, and epoched from 1 s before to 4 s after the face stimulus onset using EEGLAB  (Delorme and Makeig, 2004). First, EEG epoched data was visually inspected to remove muscle artifacts and to identify noisy electrodes that were interpolated to the average of adjacent electrodes. Second, independent component analysis (ICA) that excluded interpolated electrodes was performed on the epoched data and ICA components capturing eye blink artifacts were manually rejected.
A last, visual inspection was done on the resulting single epochs to exclude any remaining trials with artifacts. After trial rejections, an average of 999 ± 10 trials per subject remained. Time frequency analysis was performed using the Fieldtrip toolbox for MATLAB  (Oostenveld et al. , 2011). We were particularly interested in motor mu-bands (8-32 Hz) and thus estimated the spectral power of mu-beta band EEG oscillations using 'multitapering' time frequency transform (Slepian tapers, frequency range 8-32 Hz, five cycles, three tapers per window). The purpose of this multitapering approach is to obtain more precise power estimates by smoothing across frequencies. Note that this time-frequency transform uses a constant number of cycles per window across frequencies, hence a time window whose duration decreases inversely with increasing frequency.
Mice were anesthetized and scanned using the same parameters: 104 μm of pixel size, 49 kV, 0. 5 mm thick aluminum filter, and 0. 9° of rotation step. Total adipose tissue volume was determined between the lumbar vertebra 1 (L1) and the caudal vertebra 4 (C4), whereas intra-abdominal and subcutaneous adipose tissues areas were measured on one section at the lumbar 5 (L5) level. 3D reconstructions and analysis of bone parameters and adipose tissue areas or volumes were performed using NRecon and CTAn software (SkyScan).
statistical analyses Data are expressed as mean values ±SEM and was analyzed using the two-tailed Student's t-test. Differences were considered statistically significant at p ≤ 0. 05. We analyzed bone parameters upon OT treatment in orchidectomized (ORX) and ovariectomized (OVX) mice, animal models for male and female osteoporosis, respectively. Two weeks postsurgery, controls (male or female Sham) and ORX or OVX mice were daily injected with OT at a 1 mg/kg dose or vehicle (Ve, NaCl 0. 9%). After 8 weeks of treatment, trabecular and cortical bone parameters were analyzed using high-resolution micro-computed tomography (MicroCT). We confirmed our previous observations showing that OT treatment restored bone architecture of OVX mice  (14, 17). As expected in ORX mice, orchidectomy induced a dramatic trabecular bone loss evidenced by the significant decline in BV/TV, trabecule thickness (Tb.
OT Treatment does not significantly impact the expression of bone Molecular Markers In vivo Next, we analyzed the expression of bone remodeling markers in vivo to address the effect of OT at the molecular level. We quantified mRNA expression using real-time PCR and mRNAs isolated from total long bones of Sham and ORX mice treated with vehicle or OT. We observed that orchidectomy induced a threefold increase of RANKL/OPG mRNA ratio, indicating an increase of pro-osteoclastogenic signaling. RANKL/OPG ratio was not affected by OT treatment and was maintained at a high level compared to the Sham group (Figure  3A ). Regarding bone resorption, mRNA expression of the osteoclastogenic marker Tracp decreased upon orchidectomy and was normalized after OT treatment (Figure  3B ). As expected, similar observations were obtained for OVX mice [data not shown  (17) ].
We measured the expression of osteoblast markers such as collagen 1 alpha 1 (COL1A1) and osteocalcin. COL1A1 mRNA expression was not affected by orchidectomy but was induced by OT treatment in the ORX group (Figure  3C ). Osteocalcin expression was induced upon orchidectomy and was not restored in ORX-OT mice group (Figure  3D ). Opposite data were obtained for female mice, a decrease in COL1A and osteocalcin mRNA upon ovariectomy that were restored upon OT treatment [data not shown  (17) ].
Oxytocin Treatment does not normalize Orchidectomy-induced body Weight loss Then, we analyzed body weight and body composition in mice upon OT or Ve treatment for 8 weeks. We previously observed  that, in OVX mice displaying an increase of both body weight and fat mass, OT was able to restore both parameters to levels of the control Sham group [data not shown  (17) ]. As previously described  (21) , ORX mice displayed a dramatic loss of total body weight (≈5 g) mainly due to a decrease in lean mass, especially in muscle mass, in the absence of testosterone (Figure  4 ). However, daily OT injections for 8 weeks did not restore body weight and muscle mass compared to Sham-Ve mice (Figure  4 ).
4a ). Further, the trajectories were most variable at low ID e in the distance manipulation, and the variability decreased as ID e increased (Fig. 4b ), The inverse was observed for the width conditions. The effect of increasing ID via target distance was comparable for both tasks (Fig. 4c, d ). Decreasing target width, however, hardly affected the trajectory variability in the discrete task, but it led to an increased variability in the cyclic task. For the latter, at low ID e , that is, when a limit cycle dynamics was invariantly present, the trajectories were the least variable in the entire dataset. As for the movement organization under the different conditions, our results revealed identical topological organizations (fixed-point dynamics) under all but the cyclic-width task version at low ID e (and for one participant in the cyclic-distance task at low ID e ).
Data analysis Saccade detection was carried out by the Eyelink II system using a velocity (22°/s) and acceleration threshold (3800°/ s 2 ). Pre-saccadic pursuit velocity was calculated using twopoint differentiation of the position signal. The velocity signal was averaged for the statistical analysis ("Pre-saccadic pursuit" section), and a low-pass filter (40 ms symmetrical moving average) was applied for the graphical analysis (Fig. 1c ). The primary measure used was SRT. SRT was defined as the time between the onset of the target step and initiation of the saccade. Data from the following trials were removed prior to saccade analysis: Trials with blinks during the critical time period shortly before or after the target step missed trials (no saccade or RT greater than 800 ms), anticipatory sac- cades (RT smaller than 80 ms, see Wenban-Smith and Find- lay 1991), and inaccurate saccades with errors larger than 2° visual angle.
g. ,  Tychsen and Lisberger 1986)  but do not elucidate the causal relationship between both. A subset of trials was analyzed to examine whether the measured SRT asymmetries depend on the occurrence of PSVs. The subset comprises of trials with absolute PSVs smaller than 1°/s, i. e. , trials wherein the eye fixation was relatively static  (Guan et al. 2005 ). This analysis was only performed for 10°/s trials with target step amplitudes larger or equal to 6°, to assure that data points were available for the analysis for all participants and conditions (70 % of datapoints). The average PSV in this subset was 0. 07°/s. A 2 × 4 repeated-measures ANOVA shows significant differences in SRTs between both motion conditions also in this subset. SRTs were longer in saccades to foveopetal targets (198 ms) in comparison with saccades to foveofugal targets [185 ms, F(1, 15) = 31.
Determination of the nicotine-induced Ca 2؉ -response The duration and the total photons (TP) were determined using an automated statistical analysis of the signal script developed at the laboratory (a routine programmed in Microsoft Visual Basic/Excel, available on request). Briefly, a sliding window of 20 data points (5 s) was compared using t test with a control window of 240 data points (30 s) corresponding to the recorded resting phase before nicotine application. The response of the KCs was considered (quantified) between the time we obtained more than six of 10 consecutive p values above 0. 025 (starting response) and more than six of 10 consecutive p values below 0. 025 (end of response).
Quantitative and statistical analysis We used the Photon Viewer (2. 1) software (Science Wares) written in LabView 7. 1 (National Instruments) to analyze the imaging data. Nicotine-induced bioluminescence signals are presented as photons/s (within the ROI). Image recordings were obtained from five to 15 flies for each genotype. All statistics were done using InVivoStat (2. 1) software  (Clark et al. , 2012) , a biostatistics front-end for the open-source statistic package based on the R project (http://www. r-project. org/). The dataset was analyzed via one-way ANOVA followed by a planned comparison on the predicted means to compare the level of the selected effect using the Benjamini-Hochberg's with a rank transformation  (Benjamini and Hochberg, 1995; Benjamini et al. , 2001).
2J, LϪO ). Among the eight flies recorded under these conditions, two directly responded to forskolin application. These responses were synchronous in all MB parts, while the CCB response was made up of a single component (data not shown). The flies responding directly to forskolin, prior to nicotine application, were not taken into account for subsequent quantification. The remaining flies that were not responsive to forskolin application had double the amplitude and TP compared to controls, both in CCB and ML (Fig. 2G ), following nicotine application after 10 min. However, although the response duration increased in the CCB (120%) was not significant, it was significantly increased in the ML (236%) (Fig. 2N, O ). In addition, we stimulated cAMP effectors with the membrane-permeable PDE-resistant cAMP analog 8Br-cAMP (Fig.
[UAS-cngc-RNAi]: cngc-RNAi(1)ϭVDRC-101745-KK, cngc-RNAi(2)ϭ VDRC-28625-GD, P[UAS-cngl-RNAi]: cngl-RNAi(1)ϭVDRC-102411-KK, cngl-RNAi(2)ϭVDRC-40964-GD, from two different collections from  Vienna Drosophila Resource Center (VDRC)  and from  R. Ueda  ( National Institute of Genetics (NIG ), Mishima, specifically in the MBs. We use the P[UAS-G ␣s ‫]ء‬ provided by  C. O'Kane (Department of Genetics , This work was supported by  French ANR-Blanc Neurosciences 2011 (Fly-BrainImaging) , the  NeRF (Conseil Régional Ile-De France) , the  IFR-144 , the  Physique-Chimie-Biologie Interface Program  of the CNRS (2009), and by the  CNRS, France. We thank the  VDRC Stock Center (Vienna, Austria ) and the  NIG (Japan)  for the various RNAi lines. We thank  Seth Tomchik  ( Scripps Research Institute, Jupiter, FL ) for his critical reading and valuable advice and  Arianna Lark  for final editing.