dataesr/scientific-paragraphs-categorization huggingface dataset:
This work has been supported by the  Fondation pour la Recherche Medicale (FRM)  ('Equipe FRM 2010' grant to  Lionel Naccache ), by the  Institut pour le Cerveau et la Moe ¨lle epiniere  ( ICM Institute, Paris, France ), by  INSERM , by  AP-HP , by the  program ''Investissement d'Avenir   ANR-10-AIHU 06  of the  French Government '', by the  IHU-A-ICM  (' Investissement d'avenir' program ,  ANR-10-IAIHU-06 ), by  Assistance Publique -Hopitaux de Paris (AP-HP) Departement de la Recherche Clinique et du Developpement (DRCD)  ('' RESPIRONDINE '' project), by  Association Francaise du Syndrome d'Ondine (AFSO). The research leading to these results has received funding from the  program ''Investissements d'avenir' '  ANR-10-IAIHU-06. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We also examined tissues for FMRP (mouse T1A; 1:200) expression and ubiquitin-positive inclusions (DAKO, ZO458; 1:200). Consistent with our previous report  [7] , ubiquitin-positive intranuclear inclusions were identified along with a normal distribution of FMRP (data not shown). Intranuclear FMRpolyG-positive inclusions could be detected in all organs examined (Figure  1c -h, Additional file 2: Figure  S2c-h ). No control tissues showed any FMRpolyG-positive inclusions (data not shown). Colocalization of ubiquitin-and FMRpolyG-positive inclusions was visualized and quantified by immunofluorescent double staining using antibodies against ubiquitin and FMRpolyG (8FM) (Figure  2a-f ). For hippocampus, cerebellum and the non-CNS organs most inclusions are positive for both FMRpolyG and ubiquitin, although some rare inclusions positive for only one of the proteins could also be detected (Figure  2g , n = 100 inclusions).
Acknowledgements The authors wish to acknowledge the contribution of  Tom de Vries Lentsch  and  Nathan Wubben. This work was supported by the  Dutch Brain Foundation  project number  2012(1)101  and  French Muscular Dystrophy Association  project number  16649  [to RW], by the  National Institutes of Health  grant number  NINDS NS079775  [to RFB and RW], by  E-Rare  project number  40-42900-98-1001/113301201  from  ZonMW  [to RKH], by  E-Rare  " CURE FXTAS " from  ANR [NCB]  and  ERC "RNA DISEASES" [NCB ].
Similarly, different clusters of activation within the precentral sulcus were found in other studies  (Corbetta et al. , 1998; Beauchamp et al. , 2001). A meta-analysis performed on PET and fMRI datasets confirmed that for both visually and voluntarilytriggered saccades, the FEF lies in the pre-central sulcus close to its intersection with the superior frontal sulcus, potentially extending onto the superior and inferior subregions of the superficial portions of the pre-central gyrus  (Grosbras et al. , 2005). It should be noted, however, that some recent studies localized the superior FEF within the ventral portion of the superior pre-central sulcus, either at the end or at the most posterior region of the middle frontal gyrus, instead of at the level of its intersection with the superior frontal sulcus  (Amiez et al.
RECONCILING NON-HUMAN PRIMATES' AND HUMANS' LOCATIONS FOR THE FEF? In brief, the non-human primate FEF, localized mainly thanks to microstimulation studies, lies in a more rostral location Color codes as follows. Green: meta-analysis of PET studies from  Paus (1996) ; Blue: fMRI study of  Luna et al. (1998) ; Red: fMRI study of  Petit and Haxby (1999) ; Yellow: MEG study of  Ioannides et al. (2004) ; Purple: coordinates estimated by  Tehovnik et al. (2000)  based on the PET study of  Kawashima et al. (1998). A sphere of 1 cm radius is positioned at the center of FEF activation from each study. SPM (Statistical Parametric Mapping, http://www. fil. ion. ucl. ac. uk/spm/) with MarsBar toolbox was used to design the spheres and MRIcroGL software (http://www. mccauslandcenter. sc. edu/mricrogl/) was used for glass brain illustration.
Inter-trial (phase) coherence (ITC) ITC is a time-frequency domain magnitude that indicates the degree of phase synchronization at a particular latency and frequency to a set of experimental events to which EEG data trials are time locked. This measure, also called 'phase locking factor' in Tallon-Baudry et al. [22] , is defined as: ITC(f ,t)~1 n X n k{1 F k (f ,t) F k (f ,t)j j ð2Þ where |N| represents the complex norm. The ITC measure takes values between 0 and 1. A value of 0 represents an absence of phase synchronization between EEG data and the time locking events; a value of 1 indicates perfect phase synchronization.
Acknowledgments We thank  M. Lipshits  for help with the experiments and fruitful discussions, and  E. Toussaint ,  T. D'Angelo ,  E. Hortmanns , and  M. Petieau , for expert technical assistance. The authors would like to thank the cosmonauts who participated in this experiment, and the personnel at  ESA ,  CNES ,  Star City  and TSUP who made this space experiment possible, especially  D. Chaput ,  E. Lorigny , and  V. Grachev.
The ability of TUG469 and indomethacin to affect interaction between the mouse GPR40 and ␤-arrestin-2 constructs was assessed using a bioluminescence resonance energy transfer-based method  (16). The resulting concentration-response data were fit to 3-parameter sigmoid curves using GraphPad Prism version 5. 0 (GraphPad Software, Inc. , San Diego, CA). Plasma Measurements-Glycerol, NEFA, plasma glucose, alanine aminotransferase, and aspartate transaminase concentrations in plasma were analyzed with commercially available enzymatic kits (Dialab, Vienna, Austria) using an autoanalyzer (MaxMat SA, Montpellier, France). An insulin mouse ultrasensitive ELISA kit (DRG Diagnostics GmbH) was used for quantitative determination of insulin in plasma. A homeostasis model assessment of insulin resistance (HOMA-IR) was calculated from fasting insulin and glucose concentrations using the formula, plasma glucose (mmol/liter) ϫ plasma insulin (microunits/ml)/22.
E. unless otherwise indicated. Statistical analyses of physiological and gene expression data were performed with GraphPad Prism version 5. 0 (GraphPad Software, Inc. ), and Dixon's Q-test was used to screen for outliers. One-way analysis of variance was used to compare differences between the experimental groups, followed by Fisher's least significant difference test unless other-wise indicated. A significance level of p Յ 0. 05 was used for all tests. Statistical significances are denoted with asterisks as follows: *, p Յ 0. 05; **, p Յ 0. 01; ***, p Յ 0. 001.
* This work was supported, in whole or in part, by  National Institutes of Health  Grant  RO1DK090492  (to F. G. H. ). This work was also supported by the  European Union   FP7  project  DIABAT  ( HEALTH-F2-2011-278373 ), the  Danish Natural Science Research Council , the  Novo Nordisk Foundation , the  Carlsberg Foundation , the  SHARE Cross Faculty Ph. D. Initiative of the University of Copenhagen , and  NIFES. Part of this work was carried out as a part of the research program of the  Danish Obesity Research Centre (Dan-ORC). DanORC is supported by  Danish Council for Strategic Research  Grant  2101 06 0005. Part of this work was also supported by  Danish Council for Strategic Research  Grant  11-116196  for the  FFARMED  project. The laboratory of F. G. H. is supported by  Juvenile Diabetes Research Foundation  Grant  1-2009-337.
6 Na-aspartate, 7 MgSO 4 , 3. 1 Na-pyruvate, 2. 5 KCl, 1. 25 NaH 2 PO 4 , and 0. 5 CaCl 2. The brain was split at the midline, and each hemisphere glued to a custom-made wedge at a slope of 50°. Usually, only one TC slice was obtained from each hemisphere. To verify the origins of TC inputs onto layer 4 neurons in slices at this cutting angle, we conducted a separate set of experiments recording synaptic responses to glutamate uncaging with laser-scanning photostimulation (n ϭ 8). This method, which does not stimulate fibers of passage, found that thalamic inputs onto layer 4 neurons originated specifically within the VPM nucleus (Fig. 1B-D ). As a control, we recorded inputs to layer 5A neurons and found that they originated within the posteromedial (PoM) complex (data not shown). Slices were cut on a vibratome (Integraslice 7550M; Campden Instruments) and were transferred to a chamber containing artificial CSF (ACSF) continuously perfused with carbogen.
Only neurons with the regular spiking phenotype  (McCormick et al. , 1985)  were included in the analyzed dataset. Input resistance was in the range 150 -500 M⍀, and access resistance was Ͻ10% of input resistance; recordings were discarded if access resistance was unstable or the resting membrane potential drifted by Ͼ10 mV. Data were acquired with an Axon Multiclamp 700-B amplifier (Molecular Devices), filtered at 4 -10 kHz, and sampled at 20 kHz (PCI 6040-E; National Instruments) under the control of software custom written in Matlab (MathWorks;  Pologruto et al. , 2003). Electrical stimulation. TC fibers were stimulated with a Pt-Ir concentric bipolar electrode (FHC; outer pole diameter, 200 m; inner pole diameter, 25 m) connected to a stimulus isolator (Iso Flex; A. M. P. I. ). All stimuli were generated in Matlab.
2A ). We set the final magnitude at the latter level, such that response failures to temporally isolated single stimuli were not observed over ϳ10 repetitions. This approach ensured that failures of stimulation were negligible while the number of stimulated fibers was kept low (putatively ϳ1). We did not observe any systematic difference in the occurrence of stimulation failures across depressing or facilitating synapses. In all synapses, fluctuations in success probability were anticorrelated, such that a failure tended to follow a successful synaptic event and vice versa, even at the shortest stimulus intervals. Moreover, any drift in measured success probability during the experiment resulted in the elimination of the recording from the dataset. Stimulus amplitudes were 1-15 A, toward the lower end of previously reported thresholds for TC activation and an order of magnitude lower than thresholds for antidromic activation of Prior depression enhanced facilitation.
Software for control and acquisition was written in Matlab  (Suter et al. , 2010). Synaptic input maps for individual neurons were constructed by computing the mean current amplitude, averaged over a 50 ms time window beginning 6 ms after the UV stimulus for each location. These responses were used to build an input map for each cell (Fig. 1B-D ). Histology. A subset of neurons recorded with Alexa Fluor 594 in the patch pipette was histologically processed and recovered. After wholecell recording, the slice was fixed (4% paraformaldehyde in PBS; pH 7. 3) at room temperature for 2 h. Slices were then rinsed 3 ϫ 10 min in PBS, dried, and coverslipped in a solution of Mowiol (EMD Biosciences) with N-propyl gallate. Confocal stacks ϳ80 m thick were acquired using oil-immersion 20 -40ϫ objectives in an inverted microscope (TCS SL; Leica).
Serial optical sections were set at 0. 5 m intervals. Images were processed using ImageJ (NIH, http://rsb. info. nih. gov/ij/). Stacks were viewed after maximal projection onto two dimensions. Analysis. To compute PSP amplitude, we searched for the first membrane potential peak in the window extending from 0. 5 to 12 ms after the stimulation pulse, averaged the membrane potential over five data samples (from Ϫ0. 1 to 0. 1 ms relative to the raw peak), and subtracted a baseline averaged over 2 ms immediately preceding the stimulation pulse. This short baseline effectively compensated for depolarization. Each connected pair of points (colored line) corresponds to one recording. For all connections, the response at steady state was smaller than the response from rest. C, Tuning curves for all recordings with regular stimulation at three different frequencies.
After constructing the tuning curve for irregular stimulation, a tuning curve slope (TCS; Fig. 3 ) was fit by linear regression to the initial part of the tuning curve (including only intervals shorter than the regular interval, 218 ms), to the late part of the curve (intervals longer than regular), or to the full curve. All three possibilities produced a significant negative correlation between the resulting TCS and the facilitation index (described above), but the fits based on the initial part of the tuning curve gave the tightest empirical correlation. Hence, we present results for the initial (fastest timescale, Ͻ218 ms) part of the curve. TCSs for regular stimulation (Fig. 4 ) were fit by linear regression to the curve from 2 to 12 Hz. All analyses except for part of the statistical testing were conducted in Matlab (Math-Works).
Statistical testing of diversity in the TCS and facilitation index variables across the main dataset was designed as follows. Using restricted maximum likelihood, we fit each variable (TCS, facilitation index) to a linear mixed-effects model with temperature and extracellular [Ca 2ϩ ] as fixed effects and age as a random effect. This analysis was programmed in the R language. The analysis indicated a significant effect of [Ca 2ϩ ] on TCS ( p ϭ 0. 018), although not on facilitation index ( p ϭ 0. 085). Because of this dependence of TCS on [Ca 2ϩ ], we divided the dataset into a group for each [Ca 2ϩ ] and conducted the remaining nonparametric (Spearman) correlation analyses separately for each group. Results are first presented for the [Ca 2ϩ ] ϭ 1 mM group (Fig. 3C ), and then compared for different [Ca 2ϩ ] and temperatures (Fig.
Thus, at a certain time after the first part of the stimulus train, stimulation frequency could increase, decrease, or remain unchanged (Fig. 4A ). As expected from the earlier dataset (Fig. 2C ), all connections in the new experiments were depressed from stimulus onset to steady state (Fig. 4B ). Once depression to steady state was complete and the switch to the second part of the stimulus train occurred, connections displayed variable tuning. Some connections responded more strongly to long intervals (lower frequency), while others responded more to shorter intervals (higher frequency; Fig. 4C ). This suggested that tuning curves constructed under regular stimulation might also capture STP diversity. To verify this, in each of the new experiments we measured responses to trials where we switched from regular to irregular stimulation, and computed the corresponding facilitation index, as described above.
grants from the  Spanish Ministry of Science and Innovation  ( BFU2008-03017/BFI  and  BFU2011-23049 , cofunded by the  European Regional Development Fund ;  Consolider Program   CSD2007-00023 ); the  Valencia Regional Government  ( ACOMP2010/199  and  PROMETEO/2011/086 ); the  Spanish National Research Council (CSIC) ; and the  European Commission   FP7  ( IRG 200405  and  ITN Marie Curie SyMBaD , grant agreement  238608 ). M. D. -Q. was supported by an  I3P  fellowship from the  CSIC. We thank  M. C. Castellanos  for help with statistics;  A. Faire  ´n and  C. Gil  for advice on slice orientation;  A. Valero Paternain  for experimental advice; and M. E.
EFFECTS INDUCED BY VESTIBULAR INACTIVATION ON ENTORHINAL CORTEX ACTIVITY In order to characterize how the vestibular signal influences EC activity, we studied the effects of temporary inactivation of the vestibular system on local field potentials (LFP) recorded within the dMEC. More specifically, we were interested in looking at the possible changes in oscillations in the theta band (5-12 Hz) within the dMEC neural network following disruption of the vestibular signal. According to recent models  (Burgess et al. , 2007)  and experimental data  (Brandon et al. , 2011; Koenig et al. , 2011) , these oscillations are necessary to build the grid-like firing pattern. It is therefore likely that if vestibular inactivations disrupt theta oscillations in the dMEC, it would also result in a drastic disorganization of the grid-like firing pattern of the dMEC neurons.
IV infusions were given over 60 minutes except for the 0. 01 mg/kg dose, which was given over 11. 2 minutes. Key safety data were reviewed by a blinded Dose Escalation Committee (comprising GlaxoSmithKline staff and an external expert neurologist who was experienced in ALS) before proceeding to the next dosing cohort. To ensure tolerability before proceeding, the first four subjects in all cohorts of Part 1 and the first cohort of Part 2 received treatment on consecutive days, so that only one subject was randomized and dosed within any 24-hour period. Dosing of all other subjects was not staggered. The follow-up period was at least 12 weeks for all subjects. Subjects in Part 2 were followed-up for 16 weeks and subjects receiving 15 mg/kg ozanezumab were followed-up for immunogenicity for 16-20 weeks.
Endpoints and assessments The primary endpoint was the safety and tolerability of SD or RD ozanezumab in subjects with ALS. Secondary endpoints included PK, immunogenicity, functional (clinical and electrophysiological) and biomarker analyses. Assessment timings are provided in Tables  S1  and S2. Safety. Adverse events (AEs), serious AEs (SAEs), electrocardiography (ECG), vital signs, and clinical laboratory tests (hematology and biochemistry) were monitored and assessed. ECG data for all subjects were centrally analyzed and reviewed by an independent cardiologist. Evaluation of safety signals also included any adverse effects on functional endpoints (clinical and electrophysiological) or immunogenicity. Pharmacokinetics. Evaluation of plasma ozanezumab PK was performed at various time points in both parts of the study (Tables  S1  and S2 ).
Statistical analyses There was no formal calculation of power or sample size for this early phase clinical trial. The sample size was based on safety and feasibility. Safety data, drug concentration data, and PK parameters were presented in tabular and/or graphical format and summarized descriptively. All statistical analyses were performed in SAS software version 9. 1. 3 or higher. No formal hypotheses were tested. Point estimates and corresponding 95% confidence intervals were constructed for the difference between the mean of ozanezumab and the mean of placebo, calculated as m(ozanezumab)m(placebo). A mixed effects analysis of variance model was used to assess the dose proportionality of C max , AUC 0-Week 4 , and AUC 0-' , for SD and RD ozanezumab. Estimates of slope with respect to log (dose) together with 90% confidence intervals were used to quantify the degree of non-proportionality.
IHC staining of muscle biopsies suggested that there were dosedependent changes in ozanezumab quantification and detection and that ozanezumab distributed and co-localized with Nogo-A at the site of action in skeletal muscle. Following the full distribution of ozanezumab to muscle tissue and peak co-localization approximately 1 week after dosing, the percentage of colocalisation appeared to be related to levels of ozanezumab, suggesting a possible relationship with exposure. Overall, ozanezumab was well tolerated and PK parameters were generally consistent with those of humanized monoclonal antibodies. The trends observed on functional endpoints in the present study, along with the co-localization of ozanezumab in skeletal muscle, are encouraging; these observations, along with the lack of emerging safety signals, support future studies of ozanezumab in this devastating disease, and a Phase II study of efficacy and safety of ozanezumab (NCT01753076) is currently underway.
{ Continuous Lead II ECG commenced approximately 1 hour pre-dose until 24 hours post-Dose 1 and for 6 hours post-Dose 2. 1 In cohorts 6 and 7 the pre-dose muscle biopsy and blood sample were only done when the subject had passed all screening assessments and eligibility had been reconfirmed. This meant the pre-dose biopsy and blood sample could be done at any appropriate time before Day 1. The post-dose muscle biopsy and blood sample were scheduled for collection at Week 8 (unless emerging data suggested the post-dose muscle biopsy and blood sample should have been collected at an alternative week). In cohort 8, muscle biopsies and blood sample were collected from subjects at pre-dose and at one time point after the first dose.
Acknowledgments Medical writing and editorial assistance were provided by  Louisa Pettinger  of  Fishawack Indicia Ltd. The authors wish to thank the following  GlaxoSmithKline colleagues  for their contributions to the study:  Matthew Cleveland ,  Stephen DeWall ,  Lori Enney ,  Emma Francis ,  Ole Graff ,  Fiona Kelly ,  Andrew Lloyd ,  Anthony Lynch ,  Robert Stubbs , and  Joanne Thompson Adam Taylor. The authors also wish to thank  Rajat Mohindra  and  Matt Davies  (both of  GlaxoSmithKline ) for their critical review of the manuscript.
Comitede Protection des PersonnesIle-de-France VI, Hopital La Pitie-Salpetriere, Paris, France; Health Sciences, IRB, North Carolina; Johns Hopkins Medicine IRBs, Maryland; Western IRB, Washington; IRB, Weill Cornell Medical Center, New York; and IRB for the Protection of Human Subjects, SUNY Upstate Medical University, New York, USA. This study was conducted in accordance with Good Clinical Practice and the guiding principles of the Declaration of Helsinki, and all subjects provided written informed consent. This study is registered at clinicaltrials. gov/(NCT00875446) and at http:// www. gsk-clinicalstudyregister. com (GSK ID 111330). The proto- col for this trial and supporting CONSORT checklist are available as supporting information; see Checklist S1 and Protocol S1. Comitato Etico per la Sperimentazione, Azienda Ospedaliera, Universitaria Integrata di Verona, Italy; Guy's Research Ethics Committee, St.
Metabolic assessment, food intake and activity analysis The LabMaster system (TSE Systems, Chesterfield, MO) was used to measure the circadian pattern of locomotor activity and ingestion behavior as well as indirect calorimetry. Mice were placed in mock (non-recording) LabMaster cages for 1 week prior to testing to acclimate to the novel environment. After the acclimation period, the mice were transferred to recording cages at 8am. Data are recorded starting at 6am the following day, for 3 consecutive days. Animals received water and food ad libitum and remained undisturbed by the investigator during observation. The LabMaster system consists of a combination of feeding and drinking sensors, a photobeam-based activity monitoring system, and a calorimetry system to determine O 2 consumption, CO 2 production, and respiratory exchange ratio (volume CO 2 / volume O 2 ).
Open Field Activity Mice were placed in a multi-unit open field maze (San Diego Instruments) with field chamber (50 cm long 650 cm wide) for a 30 min trial. The field chamber was digitally divided into 25 quadrants of equal size using EthoVision XT 8. 0 video tracking software (Noldus Information Technology, Leesburg, VA). The nine central quadrants are referred to as the center zone, and the 16 peripheral quadrants are referred to as the peripheral zone. EthoVision software recorded the video and scored automatically for distance traveled (cm), velocity (cm/s), and time spent in the center zone vs. the peripheral zone. Distance traveled and velocity were used as measures of ambulatory movement; the amount of time spent in the center zone vs. the peripheral zone was used as a measure of anxiety levels due to the rodent's natural thigmotaxis behavior when frightened  [12].
Running wheel Running wheel behavior was assessed using mouse activity wheel chambers (Lafayette Instruments, Lafayette, IN) with dimensions of 35. 3 cm623. 5 cm620 cm for length, width, and height, respectively. The running wheel component consisted of a metallic wheel of 12. 7 cm diameter 65. 72 cm width with 38 evenly spaced rungs for the mice to run on. Total distance and velocity run each hour by each animal was automatically recorded using Lafayette computerized counting software. The mice were given a 4 h acclimation period, before data was recorded for 12 h, from 6p to 6a.